{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 案例数据背景介绍"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1.数据来源：\n",
    "\n",
    "     Dongjin Cho，韩国蔚山国立科技学院城市与环境工程学院\n",
    "\n",
    "     Cheolhee Yoo，韩国蔚山国立科技学院城市与环境工程学院\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2.摘要：\n",
    "\n",
    "    本案例所使用的数据集包含了韩国首尔2013至2017年夏天的14个天气预报（NWP）的气象预报数据、2个现场观测数据和5个地理辅助变量。\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3.数据集信息：\n",
    "\n",
    "    这些数据是为了修正韩国气象局在首尔运行的LDAPS(Local Data Assimilation and Prediction System)模式下次日最高和最低气温预报的偏差。该数据包括2013年至2017年的夏季数据。输入数据主要由LDAPS模型的次日预报数据、当前的现场最高和最低气温以及地理辅助变量组成。此数据有两个输出数据（即第二天的最高和最低气温）；2015年至2017年期间进行了后期验证。\n",
    "    关于LDAPS模式: http://qikan.camscma.cn/jamsweb/cn/article/doi/10.11898/1001-7313.20200103?viewType=HTML\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "4.数据属性介绍：\n",
    "\n",
    "      1. station - 气象站编号: 1 - 25 \n",
    "      \n",
    "      2. Date - 记录日期: yyyy-mm-dd ('2013-06-30' to '2017-08-30') \n",
    "      \n",
    "      3. Present_Tmax - 记录日期的0-21时最高气温(Â°C): 20 - 37.6 \n",
    "      \n",
    "      4. Present_Tmin - 记录日期的0-21时最低气温(Â°C): 11.3 - 29.9 \n",
    "      \n",
    "      5. LDAPS_RHmin - LDAPSM模式预测的次日最小相对湿度 (%): 19.8 - 98.5 \n",
    "      \n",
    "      6. LDAPS_RHmax - LDAPS模式预测的次日最大相对湿度 (%): 58.9 - 100 \n",
    "      \n",
    "      7. LDAPS_Tmax_lapse - LDAPS模式预测的次日最高气温递减率 (Â°C): 17.6 - 38.5 \n",
    "      \n",
    "      8. LDAPS_Tmin_lapse - LDAPS模式预测的次日最高气温递减率 (Â°C): 14.3 - 29.6 \n",
    "      \n",
    "      9. LDAPS_WS - LDAPS 模式预测的次日平均风速 (m/s): 2.9 to 21.9 \n",
    "      \n",
    "      10. LDAPS_LH - LDAPS模式预测的次日平均潜热通量 (W/m2): -13.6 - 213.4 \n",
    "      \n",
    "      11. LDAPS_CC1 - LDAPS模式预测的次日第一个6小时平均云量 (0-5 h) (%): 0 - 0.97 \n",
    "      \n",
    "      12. LDAPS_CC2 - LDAPS模式预测的次日第二个6小时平均云量 (0-5 h) (6-11 h) (%): 0 to 0.97 \n",
    "      \n",
    "      13. LDAPS_CC3 - LDAPS模式预测的次日第三个6小时平均云量 (0-5 h) (12-17 h) (%): 0 to 0.98 \n",
    "      \n",
    "      14. LDAPS_CC4 - LDAPS模式预测的次日第四个6小时平均云量 (0-5 h) (18-23 h) (%): 0 to 0.97 \n",
    "      \n",
    "      15. LDAPS_PPT1 - LDAPS模式预测的次日第一个6小时平均降水量 (0-5 h) (%): 0 to 23.7 \n",
    "      \n",
    "      16. LDAPS_PPT2 - LDAPS模式预测的次日第二个6小时平均降水量  (6-11 h) (%): 0 to 21.6 \n",
    "      \n",
    "      17. LDAPS_PPT3 - LDAPS模式预测的次日第三个6小时平均降水量 (12-17 h) (%): 0 to 15.8 \n",
    "      \n",
    "      18. LDAPS_PPT4 - LDAPS模式预测的次日第四个6小时平均降水量  (18-23 h) (%): 0 to 16.7 \n",
    "      \n",
    "      19. lat - 维度 (Â°): 37.456 - 37.645 \n",
    "      \n",
    "      20. lon - 经度 (Â°): 126.826 - 127.135 \n",
    "      \n",
    "      21. DEM - 高程 (m): 12.4 - 212.3 \n",
    "      \n",
    "      22. Slope - 坡度 (Â°): 0.1 - 5.2 \n",
    "      \n",
    "      23. Solar radiation - 太阳辐射量 (wh/m2): 4329.5 to 5992.9 \n",
    "      \n",
    "      24. Next_Tmax - 次日最高气温 (Â°C): 17.4 to 38.9 \n",
    "      \n",
    "      25. Next_Tmin - 次日最低气温 (Â°C): 11.3 to 29.8\n",
    "      \n",
    "\n"
   ]
  },
  {
   "attachments": {
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**5. sklearn中常见的几个线性回归类库：**\n",
    "\n",
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 读取数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T01:21:36.589506Z",
     "start_time": "2020-06-16T01:21:34.243221Z"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import pandas_profiling as pp \n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "#全部行都能输出\n",
    "from IPython.core.interactiveshell import InteractiveShell\n",
    "InteractiveShell.ast_node_interactivity = \"all\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T01:21:36.939352Z",
     "start_time": "2020-06-16T01:21:36.858414Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>station</th>\n",
       "      <th>Date</th>\n",
       "      <th>Present_Tmax</th>\n",
       "      <th>Present_Tmin</th>\n",
       "      <th>LDAPS_RHmin</th>\n",
       "      <th>LDAPS_RHmax</th>\n",
       "      <th>LDAPS_Tmax_lapse</th>\n",
       "      <th>LDAPS_Tmin_lapse</th>\n",
       "      <th>LDAPS_WS</th>\n",
       "      <th>LDAPS_LH</th>\n",
       "      <th>...</th>\n",
       "      <th>LDAPS_PPT2</th>\n",
       "      <th>LDAPS_PPT3</th>\n",
       "      <th>LDAPS_PPT4</th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>DEM</th>\n",
       "      <th>Slope</th>\n",
       "      <th>Solar radiation</th>\n",
       "      <th>Next_Tmax</th>\n",
       "      <th>Next_Tmin</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>6/30/2013</td>\n",
       "      <td>28.7</td>\n",
       "      <td>21.4</td>\n",
       "      <td>58.255688</td>\n",
       "      <td>91.116364</td>\n",
       "      <td>28.074101</td>\n",
       "      <td>23.006936</td>\n",
       "      <td>6.818887</td>\n",
       "      <td>69.451805</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37.6046</td>\n",
       "      <td>126.991</td>\n",
       "      <td>212.3350</td>\n",
       "      <td>2.7850</td>\n",
       "      <td>5992.895996</td>\n",
       "      <td>29.1</td>\n",
       "      <td>21.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
       "      <td>6/30/2013</td>\n",
       "      <td>31.9</td>\n",
       "      <td>21.6</td>\n",
       "      <td>52.263397</td>\n",
       "      <td>90.604721</td>\n",
       "      <td>29.850689</td>\n",
       "      <td>24.035009</td>\n",
       "      <td>5.691890</td>\n",
       "      <td>51.937448</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37.6046</td>\n",
       "      <td>127.032</td>\n",
       "      <td>44.7624</td>\n",
       "      <td>0.5141</td>\n",
       "      <td>5869.312500</td>\n",
       "      <td>30.5</td>\n",
       "      <td>22.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.0</td>\n",
       "      <td>6/30/2013</td>\n",
       "      <td>31.6</td>\n",
       "      <td>23.3</td>\n",
       "      <td>48.690479</td>\n",
       "      <td>83.973587</td>\n",
       "      <td>30.091292</td>\n",
       "      <td>24.565633</td>\n",
       "      <td>6.138224</td>\n",
       "      <td>20.573050</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37.5776</td>\n",
       "      <td>127.058</td>\n",
       "      <td>33.3068</td>\n",
       "      <td>0.2661</td>\n",
       "      <td>5863.555664</td>\n",
       "      <td>31.1</td>\n",
       "      <td>23.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.0</td>\n",
       "      <td>6/30/2013</td>\n",
       "      <td>32.0</td>\n",
       "      <td>23.4</td>\n",
       "      <td>58.239788</td>\n",
       "      <td>96.483688</td>\n",
       "      <td>29.704629</td>\n",
       "      <td>23.326177</td>\n",
       "      <td>5.650050</td>\n",
       "      <td>65.727144</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37.6450</td>\n",
       "      <td>127.022</td>\n",
       "      <td>45.7160</td>\n",
       "      <td>2.5348</td>\n",
       "      <td>5856.964844</td>\n",
       "      <td>31.7</td>\n",
       "      <td>24.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>6/30/2013</td>\n",
       "      <td>31.4</td>\n",
       "      <td>21.9</td>\n",
       "      <td>56.174095</td>\n",
       "      <td>90.155128</td>\n",
       "      <td>29.113934</td>\n",
       "      <td>23.486480</td>\n",
       "      <td>5.735004</td>\n",
       "      <td>107.965535</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37.5507</td>\n",
       "      <td>127.135</td>\n",
       "      <td>35.0380</td>\n",
       "      <td>0.5055</td>\n",
       "      <td>5859.552246</td>\n",
       "      <td>31.2</td>\n",
       "      <td>22.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   station       Date  Present_Tmax  Present_Tmin  LDAPS_RHmin  LDAPS_RHmax  \\\n",
       "0      1.0  6/30/2013          28.7          21.4    58.255688    91.116364   \n",
       "1      2.0  6/30/2013          31.9          21.6    52.263397    90.604721   \n",
       "2      3.0  6/30/2013          31.6          23.3    48.690479    83.973587   \n",
       "3      4.0  6/30/2013          32.0          23.4    58.239788    96.483688   \n",
       "4      5.0  6/30/2013          31.4          21.9    56.174095    90.155128   \n",
       "\n",
       "   LDAPS_Tmax_lapse  LDAPS_Tmin_lapse  LDAPS_WS    LDAPS_LH  ...  LDAPS_PPT2  \\\n",
       "0         28.074101         23.006936  6.818887   69.451805  ...         0.0   \n",
       "1         29.850689         24.035009  5.691890   51.937448  ...         0.0   \n",
       "2         30.091292         24.565633  6.138224   20.573050  ...         0.0   \n",
       "3         29.704629         23.326177  5.650050   65.727144  ...         0.0   \n",
       "4         29.113934         23.486480  5.735004  107.965535  ...         0.0   \n",
       "\n",
       "   LDAPS_PPT3  LDAPS_PPT4      lat      lon       DEM   Slope  \\\n",
       "0         0.0         0.0  37.6046  126.991  212.3350  2.7850   \n",
       "1         0.0         0.0  37.6046  127.032   44.7624  0.5141   \n",
       "2         0.0         0.0  37.5776  127.058   33.3068  0.2661   \n",
       "3         0.0         0.0  37.6450  127.022   45.7160  2.5348   \n",
       "4         0.0         0.0  37.5507  127.135   35.0380  0.5055   \n",
       "\n",
       "   Solar radiation  Next_Tmax  Next_Tmin  \n",
       "0      5992.895996       29.1       21.2  \n",
       "1      5869.312500       30.5       22.5  \n",
       "2      5863.555664       31.1       23.9  \n",
       "3      5856.964844       31.7       24.3  \n",
       "4      5859.552246       31.2       22.5  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Data = pd.read_csv(r\"Dataset.csv\")\n",
    "Data.head() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 数据探索"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 基本统计信息"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T00:12:45.892284Z",
     "start_time": "2020-06-16T00:12:45.810809Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>station</th>\n",
       "      <td>7750.0</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>7.211568</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>25.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Present_Tmax</th>\n",
       "      <td>7682.0</td>\n",
       "      <td>29.768211</td>\n",
       "      <td>2.969999</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>27.800000</td>\n",
       "      <td>29.900000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>37.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Present_Tmin</th>\n",
       "      <td>7682.0</td>\n",
       "      <td>23.225059</td>\n",
       "      <td>2.413961</td>\n",
       "      <td>11.300000</td>\n",
       "      <td>21.700000</td>\n",
       "      <td>23.400000</td>\n",
       "      <td>24.900000</td>\n",
       "      <td>29.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LDAPS_RHmin</th>\n",
       "      <td>7677.0</td>\n",
       "      <td>56.759372</td>\n",
       "      <td>14.668111</td>\n",
       "      <td>19.794666</td>\n",
       "      <td>45.963543</td>\n",
       "      <td>55.039024</td>\n",
       "      <td>67.190056</td>\n",
       "      <td>98.524734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LDAPS_RHmax</th>\n",
       "      <td>7677.0</td>\n",
       "      <td>88.374804</td>\n",
       "      <td>7.192004</td>\n",
       "      <td>58.936283</td>\n",
       "      <td>84.222862</td>\n",
       "      <td>89.793480</td>\n",
       "      <td>93.743629</td>\n",
       "      <td>100.000153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LDAPS_Tmax_lapse</th>\n",
       "      <td>7677.0</td>\n",
       "      <td>29.613447</td>\n",
       "      <td>2.947191</td>\n",
       "      <td>17.624954</td>\n",
       "      <td>27.673499</td>\n",
       "      <td>29.703426</td>\n",
       "      <td>31.710450</td>\n",
       "      <td>38.542255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LDAPS_Tmin_lapse</th>\n",
       "      <td>7677.0</td>\n",
       "      <td>23.512589</td>\n",
       "      <td>2.345347</td>\n",
       "      <td>14.272646</td>\n",
       "      <td>22.089739</td>\n",
       "      <td>23.760199</td>\n",
       "      <td>25.152909</td>\n",
       "      <td>29.619342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LDAPS_WS</th>\n",
       "      <td>7677.0</td>\n",
       "      <td>7.097875</td>\n",
       "      <td>2.183836</td>\n",
       "      <td>2.882580</td>\n",
       "      <td>5.678705</td>\n",
       "      <td>6.547470</td>\n",
       "      <td>8.032276</td>\n",
       "      <td>21.857621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LDAPS_LH</th>\n",
       "      <td>7677.0</td>\n",
       "      <td>62.505019</td>\n",
       "      <td>33.730589</td>\n",
       "      <td>-13.603212</td>\n",
       "      <td>37.266753</td>\n",
       "      <td>56.865482</td>\n",
       "      <td>84.223616</td>\n",
       "      <td>213.414006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LDAPS_CC1</th>\n",
       "      <td>7677.0</td>\n",
       "      <td>0.368774</td>\n",
       "      <td>0.262458</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.146654</td>\n",
       "      <td>0.315697</td>\n",
       "      <td>0.575489</td>\n",
       "      <td>0.967277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LDAPS_CC2</th>\n",
       "      <td>7677.0</td>\n",
       "      <td>0.356080</td>\n",
       "      <td>0.258061</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.140615</td>\n",
       "      <td>0.312421</td>\n",
       "      <td>0.558694</td>\n",
       "      <td>0.968353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LDAPS_CC3</th>\n",
       "      <td>7677.0</td>\n",
       "      <td>0.318404</td>\n",
       "      <td>0.250362</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.101388</td>\n",
       "      <td>0.262555</td>\n",
       "      <td>0.496703</td>\n",
       "      <td>0.983789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LDAPS_CC4</th>\n",
       "      <td>7677.0</td>\n",
       "      <td>0.299191</td>\n",
       "      <td>0.254348</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.081532</td>\n",
       "      <td>0.227664</td>\n",
       "      <td>0.499489</td>\n",
       "      <td>0.974710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LDAPS_PPT1</th>\n",
       "      <td>7677.0</td>\n",
       "      <td>0.591995</td>\n",
       "      <td>1.945768</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.052525</td>\n",
       "      <td>23.701544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LDAPS_PPT2</th>\n",
       "      <td>7677.0</td>\n",
       "      <td>0.485003</td>\n",
       "      <td>1.762807</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.018364</td>\n",
       "      <td>21.621661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LDAPS_PPT3</th>\n",
       "      <td>7677.0</td>\n",
       "      <td>0.278200</td>\n",
       "      <td>1.161809</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.007896</td>\n",
       "      <td>15.841235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LDAPS_PPT4</th>\n",
       "      <td>7677.0</td>\n",
       "      <td>0.269407</td>\n",
       "      <td>1.206214</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000041</td>\n",
       "      <td>16.655469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lat</th>\n",
       "      <td>7752.0</td>\n",
       "      <td>37.544722</td>\n",
       "      <td>0.050352</td>\n",
       "      <td>37.456200</td>\n",
       "      <td>37.510200</td>\n",
       "      <td>37.550700</td>\n",
       "      <td>37.577600</td>\n",
       "      <td>37.645000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lon</th>\n",
       "      <td>7752.0</td>\n",
       "      <td>126.991397</td>\n",
       "      <td>0.079435</td>\n",
       "      <td>126.826000</td>\n",
       "      <td>126.937000</td>\n",
       "      <td>126.995000</td>\n",
       "      <td>127.042000</td>\n",
       "      <td>127.135000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DEM</th>\n",
       "      <td>7752.0</td>\n",
       "      <td>61.867972</td>\n",
       "      <td>54.279780</td>\n",
       "      <td>12.370000</td>\n",
       "      <td>28.700000</td>\n",
       "      <td>45.716000</td>\n",
       "      <td>59.832400</td>\n",
       "      <td>212.335000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Slope</th>\n",
       "      <td>7752.0</td>\n",
       "      <td>1.257048</td>\n",
       "      <td>1.370444</td>\n",
       "      <td>0.098475</td>\n",
       "      <td>0.271300</td>\n",
       "      <td>0.618000</td>\n",
       "      <td>1.767800</td>\n",
       "      <td>5.178230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Solar radiation</th>\n",
       "      <td>7752.0</td>\n",
       "      <td>5341.502803</td>\n",
       "      <td>429.158867</td>\n",
       "      <td>4329.520508</td>\n",
       "      <td>4999.018555</td>\n",
       "      <td>5436.345215</td>\n",
       "      <td>5728.316406</td>\n",
       "      <td>5992.895996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Next_Tmax</th>\n",
       "      <td>7725.0</td>\n",
       "      <td>30.274887</td>\n",
       "      <td>3.128010</td>\n",
       "      <td>17.400000</td>\n",
       "      <td>28.200000</td>\n",
       "      <td>30.500000</td>\n",
       "      <td>32.600000</td>\n",
       "      <td>38.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Next_Tmin</th>\n",
       "      <td>7725.0</td>\n",
       "      <td>22.932220</td>\n",
       "      <td>2.487613</td>\n",
       "      <td>11.300000</td>\n",
       "      <td>21.300000</td>\n",
       "      <td>23.100000</td>\n",
       "      <td>24.600000</td>\n",
       "      <td>29.800000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   count         mean         std          min          25%  \\\n",
       "station           7750.0    13.000000    7.211568     1.000000     7.000000   \n",
       "Present_Tmax      7682.0    29.768211    2.969999    20.000000    27.800000   \n",
       "Present_Tmin      7682.0    23.225059    2.413961    11.300000    21.700000   \n",
       "LDAPS_RHmin       7677.0    56.759372   14.668111    19.794666    45.963543   \n",
       "LDAPS_RHmax       7677.0    88.374804    7.192004    58.936283    84.222862   \n",
       "LDAPS_Tmax_lapse  7677.0    29.613447    2.947191    17.624954    27.673499   \n",
       "LDAPS_Tmin_lapse  7677.0    23.512589    2.345347    14.272646    22.089739   \n",
       "LDAPS_WS          7677.0     7.097875    2.183836     2.882580     5.678705   \n",
       "LDAPS_LH          7677.0    62.505019   33.730589   -13.603212    37.266753   \n",
       "LDAPS_CC1         7677.0     0.368774    0.262458     0.000000     0.146654   \n",
       "LDAPS_CC2         7677.0     0.356080    0.258061     0.000000     0.140615   \n",
       "LDAPS_CC3         7677.0     0.318404    0.250362     0.000000     0.101388   \n",
       "LDAPS_CC4         7677.0     0.299191    0.254348     0.000000     0.081532   \n",
       "LDAPS_PPT1        7677.0     0.591995    1.945768     0.000000     0.000000   \n",
       "LDAPS_PPT2        7677.0     0.485003    1.762807     0.000000     0.000000   \n",
       "LDAPS_PPT3        7677.0     0.278200    1.161809     0.000000     0.000000   \n",
       "LDAPS_PPT4        7677.0     0.269407    1.206214     0.000000     0.000000   \n",
       "lat               7752.0    37.544722    0.050352    37.456200    37.510200   \n",
       "lon               7752.0   126.991397    0.079435   126.826000   126.937000   \n",
       "DEM               7752.0    61.867972   54.279780    12.370000    28.700000   \n",
       "Slope             7752.0     1.257048    1.370444     0.098475     0.271300   \n",
       "Solar radiation   7752.0  5341.502803  429.158867  4329.520508  4999.018555   \n",
       "Next_Tmax         7725.0    30.274887    3.128010    17.400000    28.200000   \n",
       "Next_Tmin         7725.0    22.932220    2.487613    11.300000    21.300000   \n",
       "\n",
       "                          50%          75%          max  \n",
       "station             13.000000    19.000000    25.000000  \n",
       "Present_Tmax        29.900000    32.000000    37.600000  \n",
       "Present_Tmin        23.400000    24.900000    29.900000  \n",
       "LDAPS_RHmin         55.039024    67.190056    98.524734  \n",
       "LDAPS_RHmax         89.793480    93.743629   100.000153  \n",
       "LDAPS_Tmax_lapse    29.703426    31.710450    38.542255  \n",
       "LDAPS_Tmin_lapse    23.760199    25.152909    29.619342  \n",
       "LDAPS_WS             6.547470     8.032276    21.857621  \n",
       "LDAPS_LH            56.865482    84.223616   213.414006  \n",
       "LDAPS_CC1            0.315697     0.575489     0.967277  \n",
       "LDAPS_CC2            0.312421     0.558694     0.968353  \n",
       "LDAPS_CC3            0.262555     0.496703     0.983789  \n",
       "LDAPS_CC4            0.227664     0.499489     0.974710  \n",
       "LDAPS_PPT1           0.000000     0.052525    23.701544  \n",
       "LDAPS_PPT2           0.000000     0.018364    21.621661  \n",
       "LDAPS_PPT3           0.000000     0.007896    15.841235  \n",
       "LDAPS_PPT4           0.000000     0.000041    16.655469  \n",
       "lat                 37.550700    37.577600    37.645000  \n",
       "lon                126.995000   127.042000   127.135000  \n",
       "DEM                 45.716000    59.832400   212.335000  \n",
       "Slope                0.618000     1.767800     5.178230  \n",
       "Solar radiation   5436.345215  5728.316406  5992.895996  \n",
       "Next_Tmax           30.500000    32.600000    38.900000  \n",
       "Next_Tmin           23.100000    24.600000    29.800000  "
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Data.describe().T "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T00:12:45.952260Z",
     "start_time": "2020-06-16T00:12:45.895274Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 7752 entries, 0 to 7751\n",
      "Data columns (total 25 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   station           7750 non-null   float64\n",
      " 1   Date              7750 non-null   object \n",
      " 2   Present_Tmax      7682 non-null   float64\n",
      " 3   Present_Tmin      7682 non-null   float64\n",
      " 4   LDAPS_RHmin       7677 non-null   float64\n",
      " 5   LDAPS_RHmax       7677 non-null   float64\n",
      " 6   LDAPS_Tmax_lapse  7677 non-null   float64\n",
      " 7   LDAPS_Tmin_lapse  7677 non-null   float64\n",
      " 8   LDAPS_WS          7677 non-null   float64\n",
      " 9   LDAPS_LH          7677 non-null   float64\n",
      " 10  LDAPS_CC1         7677 non-null   float64\n",
      " 11  LDAPS_CC2         7677 non-null   float64\n",
      " 12  LDAPS_CC3         7677 non-null   float64\n",
      " 13  LDAPS_CC4         7677 non-null   float64\n",
      " 14  LDAPS_PPT1        7677 non-null   float64\n",
      " 15  LDAPS_PPT2        7677 non-null   float64\n",
      " 16  LDAPS_PPT3        7677 non-null   float64\n",
      " 17  LDAPS_PPT4        7677 non-null   float64\n",
      " 18  lat               7752 non-null   float64\n",
      " 19  lon               7752 non-null   float64\n",
      " 20  DEM               7752 non-null   float64\n",
      " 21  Slope             7752 non-null   float64\n",
      " 22  Solar radiation   7752 non-null   float64\n",
      " 23  Next_Tmax         7725 non-null   float64\n",
      " 24  Next_Tmin         7725 non-null   float64\n",
      "dtypes: float64(24), object(1)\n",
      "memory usage: 1.5+ MB\n"
     ]
    }
   ],
   "source": [
    "Data.info() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 查看空值（5分）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T00:12:46.101859Z",
     "start_time": "2020-06-16T00:12:45.955239Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "station              2\n",
       "Date                 2\n",
       "Present_Tmax        70\n",
       "Present_Tmin        70\n",
       "LDAPS_RHmin         75\n",
       "LDAPS_RHmax         75\n",
       "LDAPS_Tmax_lapse    75\n",
       "LDAPS_Tmin_lapse    75\n",
       "LDAPS_WS            75\n",
       "LDAPS_LH            75\n",
       "LDAPS_CC1           75\n",
       "LDAPS_CC2           75\n",
       "LDAPS_CC3           75\n",
       "LDAPS_CC4           75\n",
       "LDAPS_PPT1          75\n",
       "LDAPS_PPT2          75\n",
       "LDAPS_PPT3          75\n",
       "LDAPS_PPT4          75\n",
       "lat                  0\n",
       "lon                  0\n",
       "DEM                  0\n",
       "Slope                0\n",
       "Solar radiation      0\n",
       "Next_Tmax           27\n",
       "Next_Tmin           27\n",
       "dtype: int64"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Data.isna().sum() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T00:12:46.301511Z",
     "start_time": "2020-06-16T00:12:46.110848Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7752 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      station   Date  Present_Tmax  Present_Tmin  LDAPS_RHmin  LDAPS_RHmax  \\\n",
       "0       False  False         False         False        False        False   \n",
       "1       False  False         False         False        False        False   \n",
       "2       False  False         False         False        False        False   \n",
       "3       False  False         False         False        False        False   \n",
       "4       False  False         False         False        False        False   \n",
       "...       ...    ...           ...           ...          ...          ...   \n",
       "7747    False  False         False         False        False        False   \n",
       "7748    False  False         False         False        False        False   \n",
       "7749    False  False         False         False        False        False   \n",
       "7750     True   True         False         False        False        False   \n",
       "7751     True   True         False         False        False        False   \n",
       "\n",
       "      LDAPS_Tmax_lapse  LDAPS_Tmin_lapse  LDAPS_WS  LDAPS_LH  ...  LDAPS_PPT2  \\\n",
       "0                False             False     False     False  ...       False   \n",
       "1                False             False     False     False  ...       False   \n",
       "2                False             False     False     False  ...       False   \n",
       "3                False             False     False     False  ...       False   \n",
       "4                False             False     False     False  ...       False   \n",
       "...                ...               ...       ...       ...  ...         ...   \n",
       "7747             False             False     False     False  ...       False   \n",
       "7748             False             False     False     False  ...       False   \n",
       "7749             False             False     False     False  ...       False   \n",
       "7750             False             False     False     False  ...       False   \n",
       "7751             False             False     False     False  ...       False   \n",
       "\n",
       "      LDAPS_PPT3  LDAPS_PPT4    lat    lon    DEM  Slope  Solar radiation  \\\n",
       "0          False       False  False  False  False  False            False   \n",
       "1          False       False  False  False  False  False            False   \n",
       "2          False       False  False  False  False  False            False   \n",
       "3          False       False  False  False  False  False            False   \n",
       "4          False       False  False  False  False  False            False   \n",
       "...          ...         ...    ...    ...    ...    ...              ...   \n",
       "7747       False       False  False  False  False  False            False   \n",
       "7748       False       False  False  False  False  False            False   \n",
       "7749       False       False  False  False  False  False            False   \n",
       "7750       False       False  False  False  False  False            False   \n",
       "7751       False       False  False  False  False  False            False   \n",
       "\n",
       "      Next_Tmax  Next_Tmin  \n",
       "0         False      False  \n",
       "1         False      False  \n",
       "2         False      False  \n",
       "3         False      False  \n",
       "4         False      False  \n",
       "...         ...        ...  \n",
       "7747      False      False  \n",
       "7748      False      False  \n",
       "7749      False      False  \n",
       "7750      False      False  \n",
       "7751      False      False  \n",
       "\n",
       "[7752 rows x 25 columns]"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 取出所有有空值的记录  （5分）\n",
    "Data.isnull()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 查看每列数据的分布状态"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T00:12:55.448321Z",
     "start_time": "2020-06-16T00:12:46.308487Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7effe51a6438>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7effe30f1b38>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7effe30fc080>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7effded44860>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7effdedf9b70>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7effdeae5390>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7effdea75048>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7effde974ac8>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7effdea2bdd8>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7effdeae5390>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7effdedbb080>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7effdebd7cc0>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7effded63d68>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7effdedaf390>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7effdecc44e0>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 绘制每列数据的直方图（分布频次）\n",
    "plot = Data.drop('Date',axis=1) # 删除'Date'列\n",
    "\n",
    "for I in plot.columns:\n",
    "    sns.displot(plot[I]) \n",
    "    plt.show() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 生成数据概况报告"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T00:15:21.940528Z",
     "start_time": "2020-06-16T00:12:55.451319Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "983b75c3762b4e83b177431d677cd2c9",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Summarize dataset:   0%|          | 0/38 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-7-183bb177293f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# 生成整体数据概要\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mdata_report\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mProfileReport\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mData\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mdata_report\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;31m# 也可以把生成的报告导出保存\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/IPython/core/displayhook.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, result)\u001b[0m\n\u001b[1;32m    260\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart_displayhook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    261\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwrite_output_prompt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 262\u001b[0;31m             \u001b[0mformat_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmd_dict\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcompute_format_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    263\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate_user_ns\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    264\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfill_exec_result\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/IPython/core/displayhook.py\u001b[0m in \u001b[0;36mcompute_format_data\u001b[0;34m(self, result)\u001b[0m\n\u001b[1;32m    149\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    150\u001b[0m         \"\"\"\n\u001b[0;32m--> 151\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshell\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdisplay_formatter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    152\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    153\u001b[0m     \u001b[0;31m# This can be set to True by the write_output_prompt method in a subclass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/IPython/core/formatters.py\u001b[0m in \u001b[0;36mformat\u001b[0;34m(self, obj, include, exclude)\u001b[0m\n\u001b[1;32m    178\u001b[0m             \u001b[0mmd\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    179\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 180\u001b[0;31m                 \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mformatter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    181\u001b[0m             \u001b[0;32mexcept\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    182\u001b[0m                 \u001b[0;31m# FIXME: log the exception\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<decorator-gen-2>\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, obj)\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/IPython/core/formatters.py\u001b[0m in \u001b[0;36mcatch_format_error\u001b[0;34m(method, self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    222\u001b[0m     \u001b[0;34m\"\"\"show traceback on failed format call\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    223\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 224\u001b[0;31m         \u001b[0mr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    225\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mNotImplementedError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    226\u001b[0m         \u001b[0;31m# don't warn on NotImplementedErrors\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/IPython/core/formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, obj)\u001b[0m\n\u001b[1;32m    343\u001b[0m             \u001b[0mmethod\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    344\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mmethod\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 345\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    346\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    347\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas_profiling/profile_report.py\u001b[0m in \u001b[0;36m_repr_html_\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    420\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_repr_html_\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    421\u001b[0m         \u001b[0;34m\"\"\"The ipython notebook widgets user interface gets called by the jupyter notebook.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 422\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_notebook_iframe\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    423\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    424\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__repr__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas_profiling/profile_report.py\u001b[0m in \u001b[0;36mto_notebook_iframe\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    400\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mwarnings\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcatch_warnings\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    401\u001b[0m             \u001b[0mwarnings\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msimplefilter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"ignore\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 402\u001b[0;31m             \u001b[0mdisplay\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mget_notebook_iframe\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    403\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    404\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mto_widgets\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas_profiling/report/presentation/flavours/widget/notebook.py\u001b[0m in \u001b[0;36mget_notebook_iframe\u001b[0;34m(config, profile)\u001b[0m\n\u001b[1;32m     73\u001b[0m         \u001b[0moutput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_notebook_iframe_src\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mconfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mprofile\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     74\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mattribute\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mIframeAttribute\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msrcdoc\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 75\u001b[0;31m         \u001b[0moutput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_notebook_iframe_srcdoc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mconfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mprofile\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     76\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     77\u001b[0m         raise ValueError(\n",
      "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas_profiling/report/presentation/flavours/widget/notebook.py\u001b[0m in \u001b[0;36mget_notebook_iframe_srcdoc\u001b[0;34m(config, profile)\u001b[0m\n\u001b[1;32m     27\u001b[0m     \u001b[0mwidth\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mconfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnotebook\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miframe\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwidth\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     28\u001b[0m     \u001b[0mheight\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mconfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnotebook\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miframe\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mheight\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 29\u001b[0;31m     \u001b[0msrc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhtml\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mescape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprofile\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_html\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     30\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     31\u001b[0m     \u001b[0miframe\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34mf'<iframe width=\"{width}\" height=\"{height}\" srcdoc=\"{src}\" frameborder=\"0\" allowfullscreen></iframe>'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas_profiling/profile_report.py\u001b[0m in \u001b[0;36mto_html\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    370\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    371\u001b[0m         \"\"\"\n\u001b[0;32m--> 372\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhtml\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    373\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    374\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mto_json\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas_profiling/profile_report.py\u001b[0m in \u001b[0;36mhtml\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    187\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mhtml\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    188\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_html\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 189\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_html\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_render_html\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    190\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_html\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    191\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas_profiling/profile_report.py\u001b[0m in \u001b[0;36m_render_html\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    289\u001b[0m         \u001b[0;32mfrom\u001b[0m \u001b[0mpandas_profiling\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreport\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpresentation\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mflavours\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mHTMLReport\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    290\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 291\u001b[0;31m         \u001b[0mreport\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreport\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    292\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    293\u001b[0m         with tqdm(\n",
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      "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas_profiling/profile_report.py\u001b[0m in \u001b[0;36mdescription_set\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    168\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msummarizer\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    169\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtypeset\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 170\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sample\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    171\u001b[0m             )\n\u001b[1;32m    172\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_description_set\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas_profiling/model/describe.py\u001b[0m in \u001b[0;36mdescribe\u001b[0;34m(config, df, summarizer, typeset, sample)\u001b[0m\n\u001b[1;32m    109\u001b[0m         \u001b[0;31m# Scatter matrix\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    110\u001b[0m         \u001b[0mpbar\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_postfix_str\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Get scatter matrix\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 111\u001b[0;31m         \u001b[0mscatter_matrix\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_scatter_matrix\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mconfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minterval_columns\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    112\u001b[0m         \u001b[0mpbar\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    113\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas_profiling/model/summary.py\u001b[0m in \u001b[0;36mget_scatter_matrix\u001b[0;34m(config, df, continuous_variables)\u001b[0m\n\u001b[1;32m    279\u001b[0m                     \u001b[0mdf_temp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdropna\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    280\u001b[0m                 scatter_matrix[x][y] = scatter_pairwise(\n\u001b[0;32m--> 281\u001b[0;31m                     \u001b[0mconfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdf_temp\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdf_temp\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    282\u001b[0m                 )\n\u001b[1;32m    283\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python3.7/contextlib.py\u001b[0m in \u001b[0;36minner\u001b[0;34m(*args, **kwds)\u001b[0m\n\u001b[1;32m     72\u001b[0m         \u001b[0;32mdef\u001b[0m \u001b[0minner\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     73\u001b[0m             \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_recreate_cm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 74\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     75\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0minner\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     76\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas_profiling/visualisation/plot.py\u001b[0m in \u001b[0;36mscatter_pairwise\u001b[0;34m(config, series1, series2, x_label, y_label)\u001b[0m\n\u001b[1;32m    294\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    295\u001b[0m         \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscatter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mseries1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mseries2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcolor\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 296\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mplot_360_n0sc0pe\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mconfig\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    297\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    298\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas_profiling/visualisation/utils.py\u001b[0m in \u001b[0;36mplot_360_n0sc0pe\u001b[0;34m(config, image_format)\u001b[0m\n\u001b[1;32m     64\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mimage_format\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"svg\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     65\u001b[0m             \u001b[0mimage_str\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mStringIO\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 66\u001b[0;31m             \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msavefig\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimage_str\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mformat\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mimage_format\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     67\u001b[0m             \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     68\u001b[0m             \u001b[0mresult_string\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mimage_str\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetvalue\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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      "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/matplotlib/figure.py\u001b[0m in \u001b[0;36msavefig\u001b[0;34m(self, fname, transparent, **kwargs)\u001b[0m\n\u001b[1;32m   3003\u001b[0m                 \u001b[0mpatch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_edgecolor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'none'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3004\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3005\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprint_figure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3006\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3007\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mtransparent\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/matplotlib/backend_bases.py\u001b[0m in \u001b[0;36mprint_figure\u001b[0;34m(self, filename, dpi, facecolor, edgecolor, orientation, format, bbox_inches, pad_inches, bbox_extra_artists, backend, **kwargs)\u001b[0m\n\u001b[1;32m   2259\u001b[0m                         \u001b[0morientation\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morientation\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2260\u001b[0m                         \u001b[0mbbox_inches_restore\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0m_bbox_inches_restore\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2261\u001b[0;31m                         **kwargs)\n\u001b[0m\u001b[1;32m   2262\u001b[0m             \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2263\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mbbox_inches\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mrestore_bbox\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_svg.py\u001b[0m in \u001b[0;36mprint_svg\u001b[0;34m(self, filename, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1331\u001b[0m                 \u001b[0mdetach\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1332\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1333\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_print_svg\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfh\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1334\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1335\u001b[0m             \u001b[0;31m# Detach underlying stream from wrapper so that it remains open in\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/matplotlib/backend_bases.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m   1667\u001b[0m             \u001b[0mkwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1668\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1669\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1670\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1671\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/matplotlib/_api/deprecation.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*inner_args, **inner_kwargs)\u001b[0m\n\u001b[1;32m    429\u001b[0m                          \u001b[0;32melse\u001b[0m \u001b[0mdeprecation_addendum\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    430\u001b[0m                 **kwargs)\n\u001b[0;32m--> 431\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minner_args\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0minner_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    432\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    433\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_svg.py\u001b[0m in \u001b[0;36m_print_svg\u001b[0;34m(self, filename, fh, dpi, bbox_inches_restore, metadata)\u001b[0m\n\u001b[1;32m   1358\u001b[0m             bbox_inches_restore=bbox_inches_restore)\n\u001b[1;32m   1359\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1360\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1361\u001b[0m         \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfinalize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1362\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m     72\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mwraps\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     73\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mdraw_wrapper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 74\u001b[0;31m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     75\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_rasterizing\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     76\u001b[0m             \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstop_rasterizing\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m     49\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     50\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 51\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     52\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     53\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/matplotlib/figure.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m   2779\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpatch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2780\u001b[0m             mimage._draw_list_compositing_images(\n\u001b[0;32m-> 2781\u001b[0;31m                 renderer, self, artists, self.suppressComposite)\n\u001b[0m\u001b[1;32m   2782\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2783\u001b[0m             \u001b[0;32mfor\u001b[0m \u001b[0msfig\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msubfigs\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/matplotlib/image.py\u001b[0m in \u001b[0;36m_draw_list_compositing_images\u001b[0;34m(renderer, parent, artists, suppress_composite)\u001b[0m\n\u001b[1;32m    130\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mnot_composite\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mhas_images\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    131\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0ma\u001b[0m \u001b[0;32min\u001b[0m \u001b[0martists\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 132\u001b[0;31m             \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    133\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    134\u001b[0m         \u001b[0;31m# Composite any adjacent images together\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m     49\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     50\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 51\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     52\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     53\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/matplotlib/_api/deprecation.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*inner_args, **inner_kwargs)\u001b[0m\n\u001b[1;32m    429\u001b[0m                          \u001b[0;32melse\u001b[0m \u001b[0mdeprecation_addendum\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    430\u001b[0m                 **kwargs)\n\u001b[0;32m--> 431\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minner_args\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0minner_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    432\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    433\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/matplotlib/axes/_base.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer, inframe)\u001b[0m\n\u001b[1;32m   2919\u001b[0m             \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstop_rasterizing\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2920\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2921\u001b[0;31m         \u001b[0mmimage\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_draw_list_compositing_images\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0martists\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2922\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2923\u001b[0m         \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose_group\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'axes'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/matplotlib/image.py\u001b[0m in \u001b[0;36m_draw_list_compositing_images\u001b[0;34m(renderer, parent, artists, suppress_composite)\u001b[0m\n\u001b[1;32m    130\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mnot_composite\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mhas_images\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    131\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0ma\u001b[0m \u001b[0;32min\u001b[0m \u001b[0martists\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 132\u001b[0;31m             \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    133\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    134\u001b[0m         \u001b[0;31m# Composite any adjacent images together\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m     49\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     50\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 51\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     52\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     53\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/matplotlib/axis.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1134\u001b[0m         \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen_group\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m__name__\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgid\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_gid\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1135\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1136\u001b[0;31m         \u001b[0mticks_to_draw\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_update_ticks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1137\u001b[0m         ticklabelBoxes, ticklabelBoxes2 = self._get_tick_bboxes(ticks_to_draw,\n\u001b[1;32m   1138\u001b[0m                                                                 renderer)\n",
      "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/matplotlib/axis.py\u001b[0m in \u001b[0;36m_update_ticks\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1023\u001b[0m         \u001b[0mmajor_locs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_majorticklocs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1024\u001b[0m         \u001b[0mmajor_labels\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmajor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformatter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat_ticks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmajor_locs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1025\u001b[0;31m         \u001b[0mmajor_ticks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_major_ticks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmajor_locs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1026\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmajor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformatter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_locs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmajor_locs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1027\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mtick\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mloc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmajor_ticks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmajor_locs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmajor_labels\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/matplotlib/axis.py\u001b[0m in \u001b[0;36mget_major_ticks\u001b[0;34m(self, numticks)\u001b[0m\n\u001b[1;32m   1352\u001b[0m         \u001b[0;32mwhile\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmajorTicks\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0mnumticks\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1353\u001b[0m             \u001b[0;31m# Update the new tick label properties from the old.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1354\u001b[0;31m             \u001b[0mtick\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_tick\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmajor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1355\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmajorTicks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtick\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1356\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_copy_tick_props\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmajorTicks\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtick\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/matplotlib/axis.py\u001b[0m in \u001b[0;36m_get_tick\u001b[0;34m(self, major)\u001b[0m\n\u001b[1;32m   2323\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2324\u001b[0m             \u001b[0mtick_kw\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_minor_tick_kw\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2325\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mYTick\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maxes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmajor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmajor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mtick_kw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2326\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2327\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mset_label_position\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mposition\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/matplotlib/axis.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    485\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    486\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 487\u001b[0;31m         \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    488\u001b[0m         \u001b[0;31m# x in axes coords, y in data coords\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    489\u001b[0m         self.tick1line.set(\n",
      "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/matplotlib/_api/deprecation.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*inner_args, **inner_kwargs)\u001b[0m\n\u001b[1;32m    429\u001b[0m                          \u001b[0;32melse\u001b[0m \u001b[0mdeprecation_addendum\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    430\u001b[0m                 **kwargs)\n\u001b[0;32m--> 431\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minner_args\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0minner_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    432\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    433\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/matplotlib/axis.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, axes, loc, label, size, width, color, tickdir, pad, labelsize, labelcolor, zorder, gridOn, tick1On, tick2On, label1On, label2On, major, labelrotation, grid_color, grid_linestyle, grid_linewidth, grid_alpha, **kw)\u001b[0m\n\u001b[1;32m     99\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maxes\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0maxes\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    100\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 101\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_loc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mloc\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    102\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_major\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmajor\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    103\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 生成整体数据概要\n",
    "data_report=pp.ProfileReport(Data) \n",
    "data_report\n",
    "\n",
    "# 也可以把生成的报告导出保存\n",
    "data_report.to_file('report.html') \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 数据预处理"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 提取数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T01:33:52.629282Z",
     "start_time": "2020-06-16T01:33:52.621285Z"
    }
   },
   "outputs": [],
   "source": [
    "# 由于本数据集的特殊性（有两个样本标签：次日最高气温和最低气温）\n",
    "#，本案例中我们只进行次日最高温预测--因此这里只选出与最高温预测相关的变量，作为建模要用的数据\n",
    "\n",
    "Max = Data[['Present_Tmax','LDAPS_RHmax','LDAPS_Tmax_lapse','LDAPS_WS','LDAPS_LH','LDAPS_CC1','LDAPS_CC2','LDAPS_CC3','LDAPS_CC4','LDAPS_PPT1','LDAPS_PPT4',\n",
    "          'lat','lon','DEM','Slope','Solar radiation','Next_Tmax']] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T01:33:56.575804Z",
     "start_time": "2020-06-16T01:33:56.551818Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Present_Tmax</th>\n",
       "      <th>LDAPS_RHmax</th>\n",
       "      <th>LDAPS_Tmax_lapse</th>\n",
       "      <th>LDAPS_WS</th>\n",
       "      <th>LDAPS_LH</th>\n",
       "      <th>LDAPS_CC1</th>\n",
       "      <th>LDAPS_CC2</th>\n",
       "      <th>LDAPS_CC3</th>\n",
       "      <th>LDAPS_CC4</th>\n",
       "      <th>LDAPS_PPT1</th>\n",
       "      <th>LDAPS_PPT4</th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>DEM</th>\n",
       "      <th>Slope</th>\n",
       "      <th>Solar radiation</th>\n",
       "      <th>Next_Tmax</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>28.7</td>\n",
       "      <td>91.116364</td>\n",
       "      <td>28.074101</td>\n",
       "      <td>6.818887</td>\n",
       "      <td>69.451805</td>\n",
       "      <td>0.233947</td>\n",
       "      <td>0.203896</td>\n",
       "      <td>0.161697</td>\n",
       "      <td>0.130928</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37.6046</td>\n",
       "      <td>126.991</td>\n",
       "      <td>212.3350</td>\n",
       "      <td>2.7850</td>\n",
       "      <td>5992.895996</td>\n",
       "      <td>29.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>31.9</td>\n",
       "      <td>90.604721</td>\n",
       "      <td>29.850689</td>\n",
       "      <td>5.691890</td>\n",
       "      <td>51.937448</td>\n",
       "      <td>0.225508</td>\n",
       "      <td>0.251771</td>\n",
       "      <td>0.159444</td>\n",
       "      <td>0.127727</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37.6046</td>\n",
       "      <td>127.032</td>\n",
       "      <td>44.7624</td>\n",
       "      <td>0.5141</td>\n",
       "      <td>5869.312500</td>\n",
       "      <td>30.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>31.6</td>\n",
       "      <td>83.973587</td>\n",
       "      <td>30.091292</td>\n",
       "      <td>6.138224</td>\n",
       "      <td>20.573050</td>\n",
       "      <td>0.209344</td>\n",
       "      <td>0.257469</td>\n",
       "      <td>0.204091</td>\n",
       "      <td>0.142125</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37.5776</td>\n",
       "      <td>127.058</td>\n",
       "      <td>33.3068</td>\n",
       "      <td>0.2661</td>\n",
       "      <td>5863.555664</td>\n",
       "      <td>31.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>32.0</td>\n",
       "      <td>96.483688</td>\n",
       "      <td>29.704629</td>\n",
       "      <td>5.650050</td>\n",
       "      <td>65.727144</td>\n",
       "      <td>0.216372</td>\n",
       "      <td>0.226002</td>\n",
       "      <td>0.161157</td>\n",
       "      <td>0.134249</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37.6450</td>\n",
       "      <td>127.022</td>\n",
       "      <td>45.7160</td>\n",
       "      <td>2.5348</td>\n",
       "      <td>5856.964844</td>\n",
       "      <td>31.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>31.4</td>\n",
       "      <td>90.155128</td>\n",
       "      <td>29.113934</td>\n",
       "      <td>5.735004</td>\n",
       "      <td>107.965535</td>\n",
       "      <td>0.151407</td>\n",
       "      <td>0.249995</td>\n",
       "      <td>0.178892</td>\n",
       "      <td>0.170021</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37.5507</td>\n",
       "      <td>127.135</td>\n",
       "      <td>35.0380</td>\n",
       "      <td>0.5055</td>\n",
       "      <td>5859.552246</td>\n",
       "      <td>31.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Present_Tmax  LDAPS_RHmax  LDAPS_Tmax_lapse  LDAPS_WS    LDAPS_LH  \\\n",
       "0          28.7    91.116364         28.074101  6.818887   69.451805   \n",
       "1          31.9    90.604721         29.850689  5.691890   51.937448   \n",
       "2          31.6    83.973587         30.091292  6.138224   20.573050   \n",
       "3          32.0    96.483688         29.704629  5.650050   65.727144   \n",
       "4          31.4    90.155128         29.113934  5.735004  107.965535   \n",
       "\n",
       "   LDAPS_CC1  LDAPS_CC2  LDAPS_CC3  LDAPS_CC4  LDAPS_PPT1  LDAPS_PPT4  \\\n",
       "0   0.233947   0.203896   0.161697   0.130928         0.0         0.0   \n",
       "1   0.225508   0.251771   0.159444   0.127727         0.0         0.0   \n",
       "2   0.209344   0.257469   0.204091   0.142125         0.0         0.0   \n",
       "3   0.216372   0.226002   0.161157   0.134249         0.0         0.0   \n",
       "4   0.151407   0.249995   0.178892   0.170021         0.0         0.0   \n",
       "\n",
       "       lat      lon       DEM   Slope  Solar radiation  Next_Tmax  \n",
       "0  37.6046  126.991  212.3350  2.7850      5992.895996       29.1  \n",
       "1  37.6046  127.032   44.7624  0.5141      5869.312500       30.5  \n",
       "2  37.5776  127.058   33.3068  0.2661      5863.555664       31.1  \n",
       "3  37.6450  127.022   45.7160  2.5348      5856.964844       31.7  \n",
       "4  37.5507  127.135   35.0380  0.5055      5859.552246       31.2  "
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Max.head() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T00:15:22.290321Z",
     "start_time": "2020-06-16T00:15:22.130413Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 7752 entries, 0 to 7751\n",
      "Data columns (total 17 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   Present_Tmax      7682 non-null   float64\n",
      " 1   LDAPS_RHmax       7677 non-null   float64\n",
      " 2   LDAPS_Tmax_lapse  7677 non-null   float64\n",
      " 3   LDAPS_WS          7677 non-null   float64\n",
      " 4   LDAPS_LH          7677 non-null   float64\n",
      " 5   LDAPS_CC1         7677 non-null   float64\n",
      " 6   LDAPS_CC2         7677 non-null   float64\n",
      " 7   LDAPS_CC3         7677 non-null   float64\n",
      " 8   LDAPS_CC4         7677 non-null   float64\n",
      " 9   LDAPS_PPT1        7677 non-null   float64\n",
      " 10  LDAPS_PPT4        7677 non-null   float64\n",
      " 11  lat               7752 non-null   float64\n",
      " 12  lon               7752 non-null   float64\n",
      " 13  DEM               7752 non-null   float64\n",
      " 14  Slope             7752 non-null   float64\n",
      " 15  Solar radiation   7752 non-null   float64\n",
      " 16  Next_Tmax         7725 non-null   float64\n",
      "dtypes: float64(17)\n",
      "memory usage: 1.0 MB\n"
     ]
    }
   ],
   "source": [
    "Max.info() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T00:15:22.570160Z",
     "start_time": "2020-06-16T00:15:22.293320Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Present_Tmax</th>\n",
       "      <td>7682.0</td>\n",
       "      <td>29.768211</td>\n",
       "      <td>2.969999</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>27.800000</td>\n",
       "      <td>29.900000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>37.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LDAPS_RHmax</th>\n",
       "      <td>7677.0</td>\n",
       "      <td>88.374804</td>\n",
       "      <td>7.192004</td>\n",
       "      <td>58.936283</td>\n",
       "      <td>84.222862</td>\n",
       "      <td>89.793480</td>\n",
       "      <td>93.743629</td>\n",
       "      <td>100.000153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LDAPS_Tmax_lapse</th>\n",
       "      <td>7677.0</td>\n",
       "      <td>29.613447</td>\n",
       "      <td>2.947191</td>\n",
       "      <td>17.624954</td>\n",
       "      <td>27.673499</td>\n",
       "      <td>29.703426</td>\n",
       "      <td>31.710450</td>\n",
       "      <td>38.542255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LDAPS_WS</th>\n",
       "      <td>7677.0</td>\n",
       "      <td>7.097875</td>\n",
       "      <td>2.183836</td>\n",
       "      <td>2.882580</td>\n",
       "      <td>5.678705</td>\n",
       "      <td>6.547470</td>\n",
       "      <td>8.032276</td>\n",
       "      <td>21.857621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LDAPS_LH</th>\n",
       "      <td>7677.0</td>\n",
       "      <td>62.505019</td>\n",
       "      <td>33.730589</td>\n",
       "      <td>-13.603212</td>\n",
       "      <td>37.266753</td>\n",
       "      <td>56.865482</td>\n",
       "      <td>84.223616</td>\n",
       "      <td>213.414006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LDAPS_CC1</th>\n",
       "      <td>7677.0</td>\n",
       "      <td>0.368774</td>\n",
       "      <td>0.262458</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.146654</td>\n",
       "      <td>0.315697</td>\n",
       "      <td>0.575489</td>\n",
       "      <td>0.967277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LDAPS_CC2</th>\n",
       "      <td>7677.0</td>\n",
       "      <td>0.356080</td>\n",
       "      <td>0.258061</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.140615</td>\n",
       "      <td>0.312421</td>\n",
       "      <td>0.558694</td>\n",
       "      <td>0.968353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LDAPS_CC3</th>\n",
       "      <td>7677.0</td>\n",
       "      <td>0.318404</td>\n",
       "      <td>0.250362</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.101388</td>\n",
       "      <td>0.262555</td>\n",
       "      <td>0.496703</td>\n",
       "      <td>0.983789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LDAPS_CC4</th>\n",
       "      <td>7677.0</td>\n",
       "      <td>0.299191</td>\n",
       "      <td>0.254348</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.081532</td>\n",
       "      <td>0.227664</td>\n",
       "      <td>0.499489</td>\n",
       "      <td>0.974710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LDAPS_PPT1</th>\n",
       "      <td>7677.0</td>\n",
       "      <td>0.591995</td>\n",
       "      <td>1.945768</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.052525</td>\n",
       "      <td>23.701544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LDAPS_PPT4</th>\n",
       "      <td>7677.0</td>\n",
       "      <td>0.269407</td>\n",
       "      <td>1.206214</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000041</td>\n",
       "      <td>16.655469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lat</th>\n",
       "      <td>7752.0</td>\n",
       "      <td>37.544722</td>\n",
       "      <td>0.050352</td>\n",
       "      <td>37.456200</td>\n",
       "      <td>37.510200</td>\n",
       "      <td>37.550700</td>\n",
       "      <td>37.577600</td>\n",
       "      <td>37.645000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lon</th>\n",
       "      <td>7752.0</td>\n",
       "      <td>126.991397</td>\n",
       "      <td>0.079435</td>\n",
       "      <td>126.826000</td>\n",
       "      <td>126.937000</td>\n",
       "      <td>126.995000</td>\n",
       "      <td>127.042000</td>\n",
       "      <td>127.135000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DEM</th>\n",
       "      <td>7752.0</td>\n",
       "      <td>61.867972</td>\n",
       "      <td>54.279780</td>\n",
       "      <td>12.370000</td>\n",
       "      <td>28.700000</td>\n",
       "      <td>45.716000</td>\n",
       "      <td>59.832400</td>\n",
       "      <td>212.335000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Slope</th>\n",
       "      <td>7752.0</td>\n",
       "      <td>1.257048</td>\n",
       "      <td>1.370444</td>\n",
       "      <td>0.098475</td>\n",
       "      <td>0.271300</td>\n",
       "      <td>0.618000</td>\n",
       "      <td>1.767800</td>\n",
       "      <td>5.178230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Solar radiation</th>\n",
       "      <td>7752.0</td>\n",
       "      <td>5341.502803</td>\n",
       "      <td>429.158867</td>\n",
       "      <td>4329.520508</td>\n",
       "      <td>4999.018555</td>\n",
       "      <td>5436.345215</td>\n",
       "      <td>5728.316406</td>\n",
       "      <td>5992.895996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Next_Tmax</th>\n",
       "      <td>7725.0</td>\n",
       "      <td>30.274887</td>\n",
       "      <td>3.128010</td>\n",
       "      <td>17.400000</td>\n",
       "      <td>28.200000</td>\n",
       "      <td>30.500000</td>\n",
       "      <td>32.600000</td>\n",
       "      <td>38.900000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   count         mean         std          min          25%  \\\n",
       "Present_Tmax      7682.0    29.768211    2.969999    20.000000    27.800000   \n",
       "LDAPS_RHmax       7677.0    88.374804    7.192004    58.936283    84.222862   \n",
       "LDAPS_Tmax_lapse  7677.0    29.613447    2.947191    17.624954    27.673499   \n",
       "LDAPS_WS          7677.0     7.097875    2.183836     2.882580     5.678705   \n",
       "LDAPS_LH          7677.0    62.505019   33.730589   -13.603212    37.266753   \n",
       "LDAPS_CC1         7677.0     0.368774    0.262458     0.000000     0.146654   \n",
       "LDAPS_CC2         7677.0     0.356080    0.258061     0.000000     0.140615   \n",
       "LDAPS_CC3         7677.0     0.318404    0.250362     0.000000     0.101388   \n",
       "LDAPS_CC4         7677.0     0.299191    0.254348     0.000000     0.081532   \n",
       "LDAPS_PPT1        7677.0     0.591995    1.945768     0.000000     0.000000   \n",
       "LDAPS_PPT4        7677.0     0.269407    1.206214     0.000000     0.000000   \n",
       "lat               7752.0    37.544722    0.050352    37.456200    37.510200   \n",
       "lon               7752.0   126.991397    0.079435   126.826000   126.937000   \n",
       "DEM               7752.0    61.867972   54.279780    12.370000    28.700000   \n",
       "Slope             7752.0     1.257048    1.370444     0.098475     0.271300   \n",
       "Solar radiation   7752.0  5341.502803  429.158867  4329.520508  4999.018555   \n",
       "Next_Tmax         7725.0    30.274887    3.128010    17.400000    28.200000   \n",
       "\n",
       "                          50%          75%          max  \n",
       "Present_Tmax        29.900000    32.000000    37.600000  \n",
       "LDAPS_RHmax         89.793480    93.743629   100.000153  \n",
       "LDAPS_Tmax_lapse    29.703426    31.710450    38.542255  \n",
       "LDAPS_WS             6.547470     8.032276    21.857621  \n",
       "LDAPS_LH            56.865482    84.223616   213.414006  \n",
       "LDAPS_CC1            0.315697     0.575489     0.967277  \n",
       "LDAPS_CC2            0.312421     0.558694     0.968353  \n",
       "LDAPS_CC3            0.262555     0.496703     0.983789  \n",
       "LDAPS_CC4            0.227664     0.499489     0.974710  \n",
       "LDAPS_PPT1           0.000000     0.052525    23.701544  \n",
       "LDAPS_PPT4           0.000000     0.000041    16.655469  \n",
       "lat                 37.550700    37.577600    37.645000  \n",
       "lon                126.995000   127.042000   127.135000  \n",
       "DEM                 45.716000    59.832400   212.335000  \n",
       "Slope                0.618000     1.767800     5.178230  \n",
       "Solar radiation   5436.345215  5728.316406  5992.895996  \n",
       "Next_Tmax           30.500000    32.600000    38.900000  "
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Max.describe().T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 填充缺失值（10分）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>False</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7747</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7748</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7749</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7750</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7751</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7752 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Present_Tmax  LDAPS_RHmax  LDAPS_Tmax_lapse  LDAPS_WS  LDAPS_LH  \\\n",
       "0            False        False             False     False     False   \n",
       "1            False        False             False     False     False   \n",
       "2            False        False             False     False     False   \n",
       "3            False        False             False     False     False   \n",
       "4            False        False             False     False     False   \n",
       "...            ...          ...               ...       ...       ...   \n",
       "7747         False        False             False     False     False   \n",
       "7748         False        False             False     False     False   \n",
       "7749         False        False             False     False     False   \n",
       "7750         False        False             False     False     False   \n",
       "7751         False        False             False     False     False   \n",
       "\n",
       "      LDAPS_CC1  LDAPS_CC2  LDAPS_CC3  LDAPS_CC4  LDAPS_PPT1  LDAPS_PPT4  \\\n",
       "0         False      False      False      False       False       False   \n",
       "1         False      False      False      False       False       False   \n",
       "2         False      False      False      False       False       False   \n",
       "3         False      False      False      False       False       False   \n",
       "4         False      False      False      False       False       False   \n",
       "...         ...        ...        ...        ...         ...         ...   \n",
       "7747      False      False      False      False       False       False   \n",
       "7748      False      False      False      False       False       False   \n",
       "7749      False      False      False      False       False       False   \n",
       "7750      False      False      False      False       False       False   \n",
       "7751      False      False      False      False       False       False   \n",
       "\n",
       "        lat    lon    DEM  Slope  Solar radiation  Next_Tmax  \n",
       "0     False  False  False  False            False      False  \n",
       "1     False  False  False  False            False      False  \n",
       "2     False  False  False  False            False      False  \n",
       "3     False  False  False  False            False      False  \n",
       "4     False  False  False  False            False      False  \n",
       "...     ...    ...    ...    ...              ...        ...  \n",
       "7747  False  False  False  False            False      False  \n",
       "7748  False  False  False  False            False      False  \n",
       "7749  False  False  False  False            False      False  \n",
       "7750  False  False  False  False            False      False  \n",
       "7751  False  False  False  False            False      False  \n",
       "\n",
       "[7752 rows x 17 columns]"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Max.isnull()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T01:34:07.620687Z",
     "start_time": "2020-06-16T01:34:07.603697Z"
    },
    "code_folding": [],
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('int64')"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 用每列的均值填充其缺失值\n",
    "a = Max.isnull().sum() \n",
    "a.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "col = [k for k,v in a.items() if v>0]\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Present_Tmax',\n",
       " 'LDAPS_RHmax',\n",
       " 'LDAPS_Tmax_lapse',\n",
       " 'LDAPS_WS',\n",
       " 'LDAPS_LH',\n",
       " 'LDAPS_CC1',\n",
       " 'LDAPS_CC2',\n",
       " 'LDAPS_CC3',\n",
       " 'LDAPS_CC4',\n",
       " 'LDAPS_PPT1',\n",
       " 'LDAPS_PPT4',\n",
       " 'Next_Tmax']"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "col"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Present_Tmax        29.768211\n",
       "LDAPS_RHmax         88.374804\n",
       "LDAPS_Tmax_lapse    29.613447\n",
       "LDAPS_WS             7.097875\n",
       "LDAPS_LH            62.505019\n",
       "LDAPS_CC1            0.368774\n",
       "LDAPS_CC2            0.356080\n",
       "LDAPS_CC3            0.318404\n",
       "LDAPS_CC4            0.299191\n",
       "LDAPS_PPT1           0.591995\n",
       "LDAPS_PPT4           0.269407\n",
       "Next_Tmax           30.274887\n",
       "dtype: float64"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#求出各组的平均值\n",
    "\n",
    "Max[col].mean()\n",
    "dic = dict(zip( Max[col].mean().index ,  Max[col].mean() ))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "Max = Max.fillna(dic)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T01:34:10.245846Z",
     "start_time": "2020-06-16T01:34:10.235840Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Present_Tmax        0\n",
       "LDAPS_RHmax         0\n",
       "LDAPS_Tmax_lapse    0\n",
       "LDAPS_WS            0\n",
       "LDAPS_LH            0\n",
       "LDAPS_CC1           0\n",
       "LDAPS_CC2           0\n",
       "LDAPS_CC3           0\n",
       "LDAPS_CC4           0\n",
       "LDAPS_PPT1          0\n",
       "LDAPS_PPT4          0\n",
       "lat                 0\n",
       "lon                 0\n",
       "DEM                 0\n",
       "Slope               0\n",
       "Solar radiation     0\n",
       "Next_Tmax           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Max.isnull().sum() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 标准化处理连续型特征"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T02:15:24.140777Z",
     "start_time": "2020-06-16T02:15:24.099800Z"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler \n",
    "\n",
    "#标准化处理连续型特征 （10分）\n",
    "std = StandardScaler().fit(Max)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "Max1 =Max.copy()\n",
    "\n",
    "\n",
    "\n",
    "Max1= std.transform(Max1 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "Max1 = pd.DataFrame(Max1,columns=Max.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Present_Tmax</th>\n",
       "      <th>LDAPS_RHmax</th>\n",
       "      <th>LDAPS_Tmax_lapse</th>\n",
       "      <th>LDAPS_WS</th>\n",
       "      <th>LDAPS_LH</th>\n",
       "      <th>LDAPS_CC1</th>\n",
       "      <th>LDAPS_CC2</th>\n",
       "      <th>LDAPS_CC3</th>\n",
       "      <th>LDAPS_CC4</th>\n",
       "      <th>LDAPS_PPT1</th>\n",
       "      <th>LDAPS_PPT4</th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>DEM</th>\n",
       "      <th>Slope</th>\n",
       "      <th>Solar radiation</th>\n",
       "      <th>Next_Tmax</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.361326</td>\n",
       "      <td>0.383078</td>\n",
       "      <td>-0.524889</td>\n",
       "      <td>-0.128382</td>\n",
       "      <td>0.206966</td>\n",
       "      <td>-0.516243</td>\n",
       "      <td>-0.592636</td>\n",
       "      <td>-0.629013</td>\n",
       "      <td>-0.664815</td>\n",
       "      <td>-0.30575</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>1.189286</td>\n",
       "      <td>-0.005000</td>\n",
       "      <td>2.772243</td>\n",
       "      <td>1.115004</td>\n",
       "      <td>1.517935</td>\n",
       "      <td>-0.376282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.721084</td>\n",
       "      <td>0.311586</td>\n",
       "      <td>0.080895</td>\n",
       "      <td>-0.646994</td>\n",
       "      <td>-0.314841</td>\n",
       "      <td>-0.548557</td>\n",
       "      <td>-0.406199</td>\n",
       "      <td>-0.638055</td>\n",
       "      <td>-0.677462</td>\n",
       "      <td>-0.30575</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>1.189286</td>\n",
       "      <td>0.511177</td>\n",
       "      <td>-0.315157</td>\n",
       "      <td>-0.542158</td>\n",
       "      <td>1.229950</td>\n",
       "      <td>0.072097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.619608</td>\n",
       "      <td>-0.614982</td>\n",
       "      <td>0.162936</td>\n",
       "      <td>-0.441604</td>\n",
       "      <td>-1.249283</td>\n",
       "      <td>-0.610450</td>\n",
       "      <td>-0.384009</td>\n",
       "      <td>-0.458843</td>\n",
       "      <td>-0.620575</td>\n",
       "      <td>-0.30575</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>0.653021</td>\n",
       "      <td>0.838510</td>\n",
       "      <td>-0.526218</td>\n",
       "      <td>-0.723133</td>\n",
       "      <td>1.216534</td>\n",
       "      <td>0.264260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.754909</td>\n",
       "      <td>1.133054</td>\n",
       "      <td>0.031092</td>\n",
       "      <td>-0.666247</td>\n",
       "      <td>0.095997</td>\n",
       "      <td>-0.583539</td>\n",
       "      <td>-0.506548</td>\n",
       "      <td>-0.631178</td>\n",
       "      <td>-0.651696</td>\n",
       "      <td>-0.30575</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>1.991696</td>\n",
       "      <td>0.385280</td>\n",
       "      <td>-0.297588</td>\n",
       "      <td>0.932424</td>\n",
       "      <td>1.201176</td>\n",
       "      <td>0.456422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.551957</td>\n",
       "      <td>0.248765</td>\n",
       "      <td>-0.170325</td>\n",
       "      <td>-0.627154</td>\n",
       "      <td>1.354409</td>\n",
       "      <td>-0.832287</td>\n",
       "      <td>-0.413115</td>\n",
       "      <td>-0.559990</td>\n",
       "      <td>-0.510358</td>\n",
       "      <td>-0.30575</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>0.118743</td>\n",
       "      <td>1.807917</td>\n",
       "      <td>-0.494322</td>\n",
       "      <td>-0.548433</td>\n",
       "      <td>1.207205</td>\n",
       "      <td>0.296287</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Present_Tmax  LDAPS_RHmax  LDAPS_Tmax_lapse  LDAPS_WS  LDAPS_LH  LDAPS_CC1  \\\n",
       "0     -0.361326     0.383078         -0.524889 -0.128382  0.206966  -0.516243   \n",
       "1      0.721084     0.311586          0.080895 -0.646994 -0.314841  -0.548557   \n",
       "2      0.619608    -0.614982          0.162936 -0.441604 -1.249283  -0.610450   \n",
       "3      0.754909     1.133054          0.031092 -0.666247  0.095997  -0.583539   \n",
       "4      0.551957     0.248765         -0.170325 -0.627154  1.354409  -0.832287   \n",
       "\n",
       "   LDAPS_CC2  LDAPS_CC3  LDAPS_CC4  LDAPS_PPT1  LDAPS_PPT4       lat  \\\n",
       "0  -0.592636  -0.629013  -0.664815    -0.30575   -0.224453  1.189286   \n",
       "1  -0.406199  -0.638055  -0.677462    -0.30575   -0.224453  1.189286   \n",
       "2  -0.384009  -0.458843  -0.620575    -0.30575   -0.224453  0.653021   \n",
       "3  -0.506548  -0.631178  -0.651696    -0.30575   -0.224453  1.991696   \n",
       "4  -0.413115  -0.559990  -0.510358    -0.30575   -0.224453  0.118743   \n",
       "\n",
       "        lon       DEM     Slope  Solar radiation  Next_Tmax  \n",
       "0 -0.005000  2.772243  1.115004         1.517935  -0.376282  \n",
       "1  0.511177 -0.315157 -0.542158         1.229950   0.072097  \n",
       "2  0.838510 -0.526218 -0.723133         1.216534   0.264260  \n",
       "3  0.385280 -0.297588  0.932424         1.201176   0.456422  \n",
       "4  1.807917 -0.494322 -0.548433         1.207205   0.296287  "
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Max1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "Scaled_Data = Max1.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 划分特征列和标签列"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T01:34:50.260685Z",
     "start_time": "2020-06-16T01:34:50.234704Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Present_Tmax</th>\n",
       "      <th>LDAPS_RHmax</th>\n",
       "      <th>LDAPS_Tmax_lapse</th>\n",
       "      <th>LDAPS_WS</th>\n",
       "      <th>LDAPS_LH</th>\n",
       "      <th>LDAPS_CC1</th>\n",
       "      <th>LDAPS_CC2</th>\n",
       "      <th>LDAPS_CC3</th>\n",
       "      <th>LDAPS_CC4</th>\n",
       "      <th>LDAPS_PPT1</th>\n",
       "      <th>LDAPS_PPT4</th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>DEM</th>\n",
       "      <th>Slope</th>\n",
       "      <th>Solar radiation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.361326</td>\n",
       "      <td>0.383078</td>\n",
       "      <td>-0.524889</td>\n",
       "      <td>-0.128382</td>\n",
       "      <td>0.206966</td>\n",
       "      <td>-0.516243</td>\n",
       "      <td>-0.592636</td>\n",
       "      <td>-0.629013</td>\n",
       "      <td>-0.664815</td>\n",
       "      <td>-0.30575</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>1.189286</td>\n",
       "      <td>-0.005000</td>\n",
       "      <td>2.772243</td>\n",
       "      <td>1.115004</td>\n",
       "      <td>1.517935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.721084</td>\n",
       "      <td>0.311586</td>\n",
       "      <td>0.080895</td>\n",
       "      <td>-0.646994</td>\n",
       "      <td>-0.314841</td>\n",
       "      <td>-0.548557</td>\n",
       "      <td>-0.406199</td>\n",
       "      <td>-0.638055</td>\n",
       "      <td>-0.677462</td>\n",
       "      <td>-0.30575</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>1.189286</td>\n",
       "      <td>0.511177</td>\n",
       "      <td>-0.315157</td>\n",
       "      <td>-0.542158</td>\n",
       "      <td>1.229950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.619608</td>\n",
       "      <td>-0.614982</td>\n",
       "      <td>0.162936</td>\n",
       "      <td>-0.441604</td>\n",
       "      <td>-1.249283</td>\n",
       "      <td>-0.610450</td>\n",
       "      <td>-0.384009</td>\n",
       "      <td>-0.458843</td>\n",
       "      <td>-0.620575</td>\n",
       "      <td>-0.30575</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>0.653021</td>\n",
       "      <td>0.838510</td>\n",
       "      <td>-0.526218</td>\n",
       "      <td>-0.723133</td>\n",
       "      <td>1.216534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.754909</td>\n",
       "      <td>1.133054</td>\n",
       "      <td>0.031092</td>\n",
       "      <td>-0.666247</td>\n",
       "      <td>0.095997</td>\n",
       "      <td>-0.583539</td>\n",
       "      <td>-0.506548</td>\n",
       "      <td>-0.631178</td>\n",
       "      <td>-0.651696</td>\n",
       "      <td>-0.30575</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>1.991696</td>\n",
       "      <td>0.385280</td>\n",
       "      <td>-0.297588</td>\n",
       "      <td>0.932424</td>\n",
       "      <td>1.201176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.551957</td>\n",
       "      <td>0.248765</td>\n",
       "      <td>-0.170325</td>\n",
       "      <td>-0.627154</td>\n",
       "      <td>1.354409</td>\n",
       "      <td>-0.832287</td>\n",
       "      <td>-0.413115</td>\n",
       "      <td>-0.559990</td>\n",
       "      <td>-0.510358</td>\n",
       "      <td>-0.30575</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>0.118743</td>\n",
       "      <td>1.807917</td>\n",
       "      <td>-0.494322</td>\n",
       "      <td>-0.548433</td>\n",
       "      <td>1.207205</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Present_Tmax  LDAPS_RHmax  LDAPS_Tmax_lapse  LDAPS_WS  LDAPS_LH  LDAPS_CC1  \\\n",
       "0     -0.361326     0.383078         -0.524889 -0.128382  0.206966  -0.516243   \n",
       "1      0.721084     0.311586          0.080895 -0.646994 -0.314841  -0.548557   \n",
       "2      0.619608    -0.614982          0.162936 -0.441604 -1.249283  -0.610450   \n",
       "3      0.754909     1.133054          0.031092 -0.666247  0.095997  -0.583539   \n",
       "4      0.551957     0.248765         -0.170325 -0.627154  1.354409  -0.832287   \n",
       "\n",
       "   LDAPS_CC2  LDAPS_CC3  LDAPS_CC4  LDAPS_PPT1  LDAPS_PPT4       lat  \\\n",
       "0  -0.592636  -0.629013  -0.664815    -0.30575   -0.224453  1.189286   \n",
       "1  -0.406199  -0.638055  -0.677462    -0.30575   -0.224453  1.189286   \n",
       "2  -0.384009  -0.458843  -0.620575    -0.30575   -0.224453  0.653021   \n",
       "3  -0.506548  -0.631178  -0.651696    -0.30575   -0.224453  1.991696   \n",
       "4  -0.413115  -0.559990  -0.510358    -0.30575   -0.224453  0.118743   \n",
       "\n",
       "        lon       DEM     Slope  Solar radiation  \n",
       "0 -0.005000  2.772243  1.115004         1.517935  \n",
       "1  0.511177 -0.315157 -0.542158         1.229950  \n",
       "2  0.838510 -0.526218 -0.723133         1.216534  \n",
       "3  0.385280 -0.297588  0.932424         1.201176  \n",
       "4  1.807917 -0.494322 -0.548433         1.207205  "
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = Scaled_Data.drop(['Next_Tmax'],axis=1) \n",
    "X.head() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T01:35:09.014361Z",
     "start_time": "2020-06-16T01:35:09.005367Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      -0.376282\n",
       "1       0.072097\n",
       "2       0.264260\n",
       "3       0.456422\n",
       "4       0.296287\n",
       "          ...   \n",
       "7747   -0.632499\n",
       "7748   -0.536418\n",
       "7749   -0.792634\n",
       "7750   -4.123453\n",
       "7751    2.762374\n",
       "Name: Next_Tmax, Length: 7752, dtype: float64"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y = Scaled_Data['Next_Tmax']\n",
    "Y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 创建线性回归模型并评估"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 划分数据集（5分）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T01:35:15.723572Z",
     "start_time": "2020-06-16T01:35:15.714578Z"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "\n",
    "xtrain ,xtest,ytrain , ytest=  train_test_split(X,Y,test_size=0.3,random_state=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Present_Tmax</th>\n",
       "      <th>LDAPS_RHmax</th>\n",
       "      <th>LDAPS_Tmax_lapse</th>\n",
       "      <th>LDAPS_WS</th>\n",
       "      <th>LDAPS_LH</th>\n",
       "      <th>LDAPS_CC1</th>\n",
       "      <th>LDAPS_CC2</th>\n",
       "      <th>LDAPS_CC3</th>\n",
       "      <th>LDAPS_CC4</th>\n",
       "      <th>LDAPS_PPT1</th>\n",
       "      <th>LDAPS_PPT4</th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>DEM</th>\n",
       "      <th>Slope</th>\n",
       "      <th>Solar radiation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>593</th>\n",
       "      <td>-1.376085</td>\n",
       "      <td>1.044566</td>\n",
       "      <td>-0.758505</td>\n",
       "      <td>2.397972</td>\n",
       "      <td>-0.319619</td>\n",
       "      <td>1.403786</td>\n",
       "      <td>0.558225</td>\n",
       "      <td>-0.795155</td>\n",
       "      <td>-0.150445</td>\n",
       "      <td>0.788315</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>0.653021</td>\n",
       "      <td>-0.672255</td>\n",
       "      <td>0.243650</td>\n",
       "      <td>0.372715</td>\n",
       "      <td>0.656413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6344</th>\n",
       "      <td>0.957861</td>\n",
       "      <td>0.669978</td>\n",
       "      <td>0.853678</td>\n",
       "      <td>-0.051662</td>\n",
       "      <td>1.571078</td>\n",
       "      <td>-1.395316</td>\n",
       "      <td>-1.378178</td>\n",
       "      <td>-0.621503</td>\n",
       "      <td>0.355440</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.215593</td>\n",
       "      <td>1.457418</td>\n",
       "      <td>0.158666</td>\n",
       "      <td>1.560277</td>\n",
       "      <td>2.534052</td>\n",
       "      <td>1.302602</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>571</th>\n",
       "      <td>0.315180</td>\n",
       "      <td>0.613097</td>\n",
       "      <td>-0.880037</td>\n",
       "      <td>0.863969</td>\n",
       "      <td>-0.553041</td>\n",
       "      <td>1.445315</td>\n",
       "      <td>2.078433</td>\n",
       "      <td>0.801649</td>\n",
       "      <td>0.847011</td>\n",
       "      <td>-0.301580</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>-0.685654</td>\n",
       "      <td>1.191021</td>\n",
       "      <td>-0.735149</td>\n",
       "      <td>-0.820115</td>\n",
       "      <td>0.596108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3988</th>\n",
       "      <td>0.349006</td>\n",
       "      <td>0.215510</td>\n",
       "      <td>-0.084261</td>\n",
       "      <td>0.827869</td>\n",
       "      <td>-0.961723</td>\n",
       "      <td>0.757638</td>\n",
       "      <td>1.677346</td>\n",
       "      <td>0.774230</td>\n",
       "      <td>-0.576444</td>\n",
       "      <td>-0.268061</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>-0.953787</td>\n",
       "      <td>-0.810742</td>\n",
       "      <td>-0.569309</td>\n",
       "      <td>-0.466338</td>\n",
       "      <td>-0.064254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2693</th>\n",
       "      <td>-2.221718</td>\n",
       "      <td>0.316398</td>\n",
       "      <td>-0.259833</td>\n",
       "      <td>-1.021322</td>\n",
       "      <td>-0.590147</td>\n",
       "      <td>0.132152</td>\n",
       "      <td>-0.256669</td>\n",
       "      <td>-0.584547</td>\n",
       "      <td>-0.941379</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>0.653021</td>\n",
       "      <td>-0.672255</td>\n",
       "      <td>0.243650</td>\n",
       "      <td>0.372715</td>\n",
       "      <td>-0.634146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>0.247530</td>\n",
       "      <td>0.329096</td>\n",
       "      <td>0.088062</td>\n",
       "      <td>0.929038</td>\n",
       "      <td>0.619683</td>\n",
       "      <td>-0.922191</td>\n",
       "      <td>0.220924</td>\n",
       "      <td>0.345807</td>\n",
       "      <td>1.805030</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.189799</td>\n",
       "      <td>0.118743</td>\n",
       "      <td>1.807917</td>\n",
       "      <td>-0.494322</td>\n",
       "      <td>-0.548433</td>\n",
       "      <td>1.165320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3927</th>\n",
       "      <td>-0.056898</td>\n",
       "      <td>-0.026969</td>\n",
       "      <td>0.697627</td>\n",
       "      <td>1.042327</td>\n",
       "      <td>-1.483930</td>\n",
       "      <td>0.182722</td>\n",
       "      <td>1.301834</td>\n",
       "      <td>0.814760</td>\n",
       "      <td>-0.472847</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>0.653021</td>\n",
       "      <td>0.838510</td>\n",
       "      <td>-0.526218</td>\n",
       "      <td>-0.723133</td>\n",
       "      <td>0.075806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5955</th>\n",
       "      <td>2.006446</td>\n",
       "      <td>-1.978761</td>\n",
       "      <td>1.667188</td>\n",
       "      <td>-0.406979</td>\n",
       "      <td>0.266701</td>\n",
       "      <td>-1.412018</td>\n",
       "      <td>-1.332939</td>\n",
       "      <td>-0.480934</td>\n",
       "      <td>-0.943014</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>-0.685654</td>\n",
       "      <td>0.637074</td>\n",
       "      <td>-0.133199</td>\n",
       "      <td>-0.810993</td>\n",
       "      <td>-1.345214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6936</th>\n",
       "      <td>-0.530452</td>\n",
       "      <td>-1.319506</td>\n",
       "      <td>0.350824</td>\n",
       "      <td>0.064060</td>\n",
       "      <td>-1.408462</td>\n",
       "      <td>0.070027</td>\n",
       "      <td>0.127936</td>\n",
       "      <td>0.273038</td>\n",
       "      <td>-0.653336</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>0.118743</td>\n",
       "      <td>-0.042770</td>\n",
       "      <td>1.294304</td>\n",
       "      <td>-0.484508</td>\n",
       "      <td>0.425472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5640</th>\n",
       "      <td>1.093162</td>\n",
       "      <td>0.050363</td>\n",
       "      <td>0.865340</td>\n",
       "      <td>-0.552903</td>\n",
       "      <td>0.591413</td>\n",
       "      <td>0.330312</td>\n",
       "      <td>-0.490390</td>\n",
       "      <td>0.464323</td>\n",
       "      <td>-0.998551</td>\n",
       "      <td>-0.276020</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>-1.490051</td>\n",
       "      <td>0.045358</td>\n",
       "      <td>0.376283</td>\n",
       "      <td>0.730359</td>\n",
       "      <td>-0.435855</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5426 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Present_Tmax  LDAPS_RHmax  LDAPS_Tmax_lapse  LDAPS_WS  LDAPS_LH  \\\n",
       "593      -1.376085     1.044566         -0.758505  2.397972 -0.319619   \n",
       "6344      0.957861     0.669978          0.853678 -0.051662  1.571078   \n",
       "571       0.315180     0.613097         -0.880037  0.863969 -0.553041   \n",
       "3988      0.349006     0.215510         -0.084261  0.827869 -0.961723   \n",
       "2693     -2.221718     0.316398         -0.259833 -1.021322 -0.590147   \n",
       "...            ...          ...               ...       ...       ...   \n",
       "79        0.247530     0.329096          0.088062  0.929038  0.619683   \n",
       "3927     -0.056898    -0.026969          0.697627  1.042327 -1.483930   \n",
       "5955      2.006446    -1.978761          1.667188 -0.406979  0.266701   \n",
       "6936     -0.530452    -1.319506          0.350824  0.064060 -1.408462   \n",
       "5640      1.093162     0.050363          0.865340 -0.552903  0.591413   \n",
       "\n",
       "      LDAPS_CC1  LDAPS_CC2  LDAPS_CC3  LDAPS_CC4  LDAPS_PPT1  LDAPS_PPT4  \\\n",
       "593    1.403786   0.558225  -0.795155  -0.150445    0.788315   -0.224453   \n",
       "6344  -1.395316  -1.378178  -0.621503   0.355440   -0.305750   -0.215593   \n",
       "571    1.445315   2.078433   0.801649   0.847011   -0.301580   -0.224453   \n",
       "3988   0.757638   1.677346   0.774230  -0.576444   -0.268061   -0.224453   \n",
       "2693   0.132152  -0.256669  -0.584547  -0.941379   -0.305750   -0.224453   \n",
       "...         ...        ...        ...        ...         ...         ...   \n",
       "79    -0.922191   0.220924   0.345807   1.805030   -0.305750   -0.189799   \n",
       "3927   0.182722   1.301834   0.814760  -0.472847   -0.305750   -0.224453   \n",
       "5955  -1.412018  -1.332939  -0.480934  -0.943014   -0.305750   -0.224453   \n",
       "6936   0.070027   0.127936   0.273038  -0.653336   -0.305750   -0.224453   \n",
       "5640   0.330312  -0.490390   0.464323  -0.998551   -0.276020   -0.224453   \n",
       "\n",
       "           lat       lon       DEM     Slope  Solar radiation  \n",
       "593   0.653021 -0.672255  0.243650  0.372715         0.656413  \n",
       "6344  1.457418  0.158666  1.560277  2.534052         1.302602  \n",
       "571  -0.685654  1.191021 -0.735149 -0.820115         0.596108  \n",
       "3988 -0.953787 -0.810742 -0.569309 -0.466338        -0.064254  \n",
       "2693  0.653021 -0.672255  0.243650  0.372715        -0.634146  \n",
       "...        ...       ...       ...       ...              ...  \n",
       "79    0.118743  1.807917 -0.494322 -0.548433         1.165320  \n",
       "3927  0.653021  0.838510 -0.526218 -0.723133         0.075806  \n",
       "5955 -0.685654  0.637074 -0.133199 -0.810993        -1.345214  \n",
       "6936  0.118743 -0.042770  1.294304 -0.484508         0.425472  \n",
       "5640 -1.490051  0.045358  0.376283  0.730359        -0.435855  \n",
       "\n",
       "[5426 rows x 16 columns]"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xtrain"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 建模（5分）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T01:35:18.264310Z",
     "start_time": "2020-06-16T01:35:18.247275Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "reg = LinearRegression()\n",
    "reg.fit(xtrain,ytrain)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-15T06:32:40.607647Z",
     "start_time": "2020-06-15T06:32:40.603648Z"
    }
   },
   "source": [
    "## 评估（5分）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 2.08091662e-01,  4.13677967e-02,  6.14354739e-01, -9.02657887e-02,\n",
       "        6.77547885e-02, -8.12720975e-02,  2.61977549e-02, -3.78396500e-02,\n",
       "       -9.18064289e-02,  2.07399134e-02,  4.12042623e-04, -1.66667586e-02,\n",
       "       -6.23493085e-02, -9.69284906e-02,  1.03010039e-01,  2.45462632e-02])"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.0034102594574352265"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7522855025647003"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.score(xtest,ytest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_predict = reg.predict(xtest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T01:35:23.925342Z",
     "start_time": "2020-06-16T01:35:23.911350Z"
    },
    "scrolled": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 计算MSE与RMSE（10分）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T01:22:24.025817Z",
     "start_time": "2020-06-16T01:22:24.016823Z"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "\n",
    "# MSE\n",
    "mse_predict = mean_squared_error(ytest, y_predict)\n",
    "\n",
    "# MAE\n",
    "mae_predict = mean_absolute_error(ytest, y_predict)\n",
    "\n",
    "#RMSE\n",
    "RMSE = np.sqrt(mean_squared_error(ytest,y_predict))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.248202468175818"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mse_predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.49819922538660977"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "RMSE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 模型诊断"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 绘制线性拟合图（10分）"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T00:15:24.480812Z",
     "start_time": "2020-06-16T00:15:24.344885Z"
    }
   },
   "source": [
    "'''\n",
    "从上可以看到RMSE(越小越好）的值0.5，我们通过可视化来看一下训练后的预测和真实值之间的差异：\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T01:22:30.193284Z",
     "start_time": "2020-06-16T01:22:29.785111Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1440x720 with 0 Axes>"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f4330f07e10>]"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f4330f11208>]"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f4330f07e48>"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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EAdZdN9kRr8xqvbcvwYUmr0Qzw1IOlfVcaMZ7b4stgP339y0FEQI77wzssgv7mOt7j+Yk1capciiKot4AngTwNYB9eOniOL4hjuNBcRwP6kuhw+vIeuoQCYUDD89yiDVbSNP++quxXC755hvg888dFMxoTNVJlSj9THQBUEI/LutGaZAZ5w8/AB995LaOEJrqMyyDMT90sVJWaBOeaPIk3yIQHlHZMPHFF8Prv0Tjk76z6VBmEItWqL83LiHMLYhw4N3rLS3ASy+VKkpD4Gy4jqKoO4D/AugMYIc4jn9xVVejc9FFviWoIDzlkGi2EPhMYsklgWWXLacu1aaQSe+8ecuyHEqp+Fb2yy8PrLJKOXX5bILl8Bl+s+f6/gRwSHz9DfyDJcenCsZKLGBstY3qwuj224HNNwfuuMNO/QQhi8itjBb4BJHQaPdCKOdz5ZXAZpvZ9VxoBjq5KDSKok4A7gOwLID14zj+wUU9zcLIkb4lqAjZmTetULzAijnE+s0JjpRDdV2pQWIOTZnivo7ATtmYSg0rU6eWWl2jXesqINsfR49OPr/80p0sBMGCFZC6LGhManwq9UwW4Os8XN0jZZ8P7zzSULJjxpQnSyPgynLoGgDbATgHQJ8oitbL/Nmx728iCm+ybt1qvh55ZJM9FEUnK+NWpsncuUlxp51mrUi/ZNqmkv2n7JhDqvh4+o8Zk8h7113KWaPIXuyCZp3ASXWV4cOThC+/rCyPSPG6xaHLKJdH2CPEPl/JcZ2oNFnlEAWiJmxBYxkBJHF9Cfu4Ug5t1fp5OYA3cn+LOqqzYSlc8+Zi5Vx5pTtZgoQ1A1CxW9Z8ysyalXxefLFW9oYiiEmYI+UQt3sEcdIFfPhh8qnpT2K66wlN4CR4/vnk87//1S6C1RVfeGd+7fIIe4RwD1RhqCIaE3IhI1xAY1rYlH2/F/UH6i9qOFEOxXHcP47jiPP3lYs6G5myQ6kAicWAj3qNyN79qfAyu5WJfmsAxo5NdiyYPt2vHD5iDp17LvCnPwFHHy3Is//+uObEb9q2H8/DdStTxefs2PPMvOxb65JLgFtvtV9uzXnssgvw88/y6RnciINxxWtrmgsWEA06jLYxcyawxx7FmwO4fMRUcQhqNmbPBvbcE/j0U9+S+CU7FWsUt7LrrgOuuspN2URz4WtMrtpz+tVXgcMO4x+fMwfYd9/296GEGR72DyBU8XET33YbMG5c+fVqoeJWZpGqDK4nnww89BBw333u6hBeApSkZWQoh047DbjpJuDyyzl5xowBbrsNR1z4GxxxhGJ9VQhI7bmT+pr4HHcccMABjit56CHg7ruNijgEN+KoR7ewJJBfmkXx8MorwH/+I56oZnHZLlUYgpqVN98E7r0XOPhg35L4pREthw47DPjrX31L0dw0Sn+isVmOjTdOlLI8Ro4EhgwB9t6bfbxR+ktZkHIoUEaOBN56K/mfBg8NihqNN1K0tAB33qm0LXrog056Kh07chLY7mC+OuwHHwBDh1ovtu76Ci743dgTM9HZugx53nkHGDECeO+95K+QqlkOjRsHPP20E1lMqDuPgv2ZXTd7acHePfLMM8B33/mWQh8bw+Err9jZfC70ZxWhRxwnYeVSV3ceEyfyvVcnTtSr+6OPkueRSDaAvVuZKM+QIYn1lSyffAK88EKiuJWthzBjxoxEAUpUj0Z7FtBuqXZxslsZYc7AgclnHFfQvcsXrN3KeCNF9vfsKHnLLYkf0sSJAl+kalGoHEqJIu2BVZyvpKfQZpsln5afDtziGAf2xt04FhejJgyVg6fwOusUilJwoBy0T32DDZKtlQJ70qsqhwhztt4a6NevuXcb2WST5DOw24GQoIxr9uijwD77JC8MzjuPn2677RJFztSpQI8etcd+9zvgjTfU615lleSzaKrFGip5z4cHHkhcRD77DDjzTDk5Vlyx/f8llgAGD5bLR+hz7LHAtdcCiy6aWHb4oOpjIrmVqRHHem1W1fP1Bc1sKwB1ag14jVY0qkyYkHx+/71deWT58UfrRUorhyx0NLYlQzU78M8/c97EFryiGIvFa38Iwa0sUMuhKVM4yu+q7LldhnJoxozkryzmzAGmTdPOLtPdJ09WK/Pbb/VkCQEViwmdMrP88kvd/hRW6yXCJJ02FFnYffZZ8jlnDv+YbUThH3n9ctKk5FM3tMHUqfw6Q2DmTP8xIG2QKuzT9i6TUK9t2cyd6679G6WfEuqQcqgCkOVQO8qTXIezYutFDx0K9Omjte24iLT/yKxjQ4iPEQrzzw9stJGaWxkAxDxLqSaczYj0aNOmAb16ASecUK5MJlgLTq5Cjx7AfPO5rydl//2BBRZQzibbFO+8A/TuDdxzj3IVQRBynJ95502sJniyNOEQ1FToetO7xKTv6d5Doc+ZV1ml3CG9EanafNIVRxwB9OxZ7FKqw8orh9dPdXclo2efGqQcqgA0CErCciuTSSubh4O1Qef995PPdGtrS6SWQ4XKIQO3soKCXRRqh4KL9/bb4bpsVZ30bVdVlQQA7FoO8frinDmFqx2rXXHIEIuF1ZPGyLI8zAVL2Qp3UewYmiCXB7V1QlY5lH9ZwGujRm87V1ZaRPNxxx3Jp0p8riyie61oR04fuAl9QeQh5VAFqFkX3HMPsPbaUvkefhhYbbXw36IYo2KvLJvfJ5ZHsfT6F7qVZVBuki+TaKl/xz9x1FGKeRUx3CDKnAK3MpkgwXfdBazTYyRw881WRBo4EHjqqfbvf/lLa8gsnde2l14K/P73VuRKefbZ+omGcgDBvfcGzjnHqlyqOI05pHHfVzEgtc9J2vbbA1dcYacsn4+N0B5ZBJ8y+3sUAa+9Biy9dOIWHQLZRxCvLXbaCbjoouT/adOAQw+VL/+44+p3DlRp8/PPB3bfXT49UY+PMb0KY+CPPwJLLgm8+677uqqq/Hj+eWD55dnu0CkXX8w/lsKbT/L6yRlnyMnXrJByqALUdPa99pLekWm//ZINnBrJZ1R6ACwKSO0iEIQpjp520jGHMig3ydPPtP17xZW152F7K3veVpWu4LqVcRqJ61aWYZ99gHd+WtHaPscjR9ZOkK++Grj88kwClb517LHAY49ZkStb7fXX849JcffdwOmnG8tkQmgBqWX6WtnIjh0+JvdPPAHnyusUi8apSnWYpCOqzwknJCHbUkNkFmX0zXy5oqHykUfa3YvTXXplueSS+i2uVc7l5JOB++9Xq5MgZHjuOeCbb4B//MNdHVVQkok48khg1Chg9Gh+muOPb/9f1W2Ml/7ss+Xka1ZIOVQBfJnRVW7QMXUr02X2LH2bThaasr31FvD44/W/61gOKSMx23S2QHHcUY3jzIQQkNoEg1fQMjrYQhEfeEC80kES2PrSS+019ahRwJ13SiSk3craqNJtocoXXwC33upbCnN8PNOffx54+eXy6212WNfa9fX/7LN2Nxcg8Yh96KH2ussaI6o0thBmqF7rMWOAm25yIwuPbL9/5BFg2DB7Zev09RdeAF56yZ4MADB8eOKt4hq6t8uBtrKvALpuYaZBKEO8CY0DUlucMbUVPXs2cP2/E18eE5T9bGpZbz129sKYQ4z6bE7inK9HfHVUA7cyFwibwWRV8Pe/J6ZIGoiqlRIpjoHdditMdvjhiWHR6qsDm28uLR6XVVZJAjzuu2+9ODV41qBX0a2siqy3XrKR5f77+5ZED5/P8i239C+DD5pxi+pVV03cQ/bbL/l+443tx2SNtVXe8cmUQRBZttwyefmz225JIGeXsPrhTjvxj5XFFlvYL3OttZLPdddVy+famrZyRg6BQK89K0BWOTQWi2Eqekjla+odSnRGHN2A1Ijt+O4F5FamTL7tfvml1O3IZ6IzRmNp9YwSbW7brWzqqO9lJLODRJ/++OOCBAbbmssQz5nT3kllYFjppVujz/xirJYMX3xR6/MuvfOHzXs2ivDVV8mtU0QcJ66E2e9Vo0rPpQkTks84Vm9rF+epq7wPvc1HjWJvs55nypTibdtDJX/v5vn44+I+9vnnyTbTI0fWp5W51q7cyvJxQ7JGp2X2vSqOhyHz2Wd2jeNtItOvvvii/f90LFeZcpjisu+nZf/wg7s68qTjjw/o3i4HUg5VgOzN0A9jMRBFq7kmhfXKSduXpXHwoRz6bPNDk30wyyCKcCBuwTIYjZ8xr/XirVmLtOZbdnmN/O+8I1u88sGVVgKeeYZ72Nq9wnujE0+YyI8lxKr7mGP4lfzpYGD8eCW5Zs0CBgxI4kAV4Tog9VJLATvuWJz08suTIORvYj1huhkzLMmmQCMPrdlzK3NrbluErBz64oskMOnJJxenXXZZYPHF3cvkgiuvTO7dN96oP/bMM8l4LHJhnDo1Of9VVknKufLK2uMi5VAVgqjbkJGnMCPUGTcOWG651g0ucoQwnhRd2/T5nuJDZpn+Z9pHs+fokilTkvHnz39mH3fdvnQvlwMphypA/mb4DnKzoiL9SBEhDPx5lANSE/Ixh0wueK69x7/1VVESqzyDrQAAM9DNXSV5NE9oAhZSzySK1tcKUxxJGUeNUpRHEhm3shitW+zIInCWjxElW4QokL4Rze72xi2/hIDUzz1XnCbVFY6GeEYobQFFSKFyy9MjSI3vWw0qX321OO3EiW5lsUm+H6T7ibC2M08tit57j19eaqSc5s++NzBVXtomK092qBQpsGzcN3Tv2SN9nLIeuz7bWbZ/5y2eDKM3ND2i/gD4X2/SdbUDKYcqAC/m0EsvJTfUGPQT5o/nn7/2bfrrrycZs7aWrHwh3mQLLyyXrkj47EhkY1SSKePjj+XSxTFaPv8CUQRc8bcvccIJQN++meMrr6wU/IIXc2i33dT9g7kI2tt5XBTD6zcAmT3Wl1oKOOIIp/WVSn4WPmdO8v+119YkiyIAV11V6rlJxxxyzGmnGWS27FYmS6ronQuX5oBEHp14KC5vqUbcraxKsorgXff03pVxn1MpNyVtv5dfbp0fjuGnKfrNJqyA1GXtG8Jqs3T+nOWnn5Lf7rnHTr2NRKPdlyIDZFd1ilz4830xipLdNUMgq8hOXzh16WKnbNV+1bUrMGlS/e/ffFNb3jPPJG2oaEhOtELKoQrAUw6lW0O/hg2Zx9sshxDV7g/6738nn88/b0nCEvmRMSqIcOhWplxEUSj/zCuN2U88CwA4/op+uOii3JvSESOA226TrpbnVvbAA8Dbb9f+pt0s+Z3JMnF3Qtxuu40owhdZ64uvvgKuuYaXtJYyZ0u26kqDQJx0Uv2x446zU0cG2SCktlbQOorIyy5LPrVMvz3tVhaicqhKOlNd4lj+PF0OD4aerUESsmw68K5/p9ZtYEQxT0zevqd502DQr7xSe9wXWdmMNyooQKb90vlzlvR96fnnm8tAlINuDLjsrnquyMv2yCPyaYHEfTwEslbVaayhzp39yAJIxMlEu7utREQGggEphyqA6SQzRqT/mkqRp59W0lsUM3Vq4uzcGulQqGhgvdbNW0elT4Zff03KtRBsN78gvfjiZFvHlPQUZs4pWMhFEV7Eprjxs00Rt9hbWXgJSF0VZOTOBxu3vB/vhAnJWyzmLfraa1xlVR4psVJNc06pEUXga6Eff1yqfl2ZYkS44/vf4sknWQfV+lWMSLsvasXnKVM5dNRRbX1RVjmku9C6H7u2bUGtSlWHAhluvx3sfipAWkGa4YUX7G633MjXpGq03bsM5ZDM/VoUaDq91vPMk3yWNPUrpEylaktLEivnhBPslUkQusQxcOKJbCu+lA8/BC64oD4fi7PPBj75JPm/bIWvrOVQaMp+GgPUoK3sK4Bup66xHNIIza9zc2+zTfL5f/+nnpfJWWcl6vNll+W7+7AETWdEv/99+/YEWW68MSm3UyfglFOS3yyNHscfX1vcGWckVS3/u1VxWEHezfEi8Caw3+6JQsDmWzRuWZnz1q2vLthwRmFW+e22n3kGwM71v1vatuioo5Jt2DfYANh119zBjTZKPu+6q7B4YcyhtO5UAZSTJYrA345kypTCunXIxhz64yenANtJNqnHWYeq5ZBVUa+4IvEvPfVU55ZDu+N+YBc3E6oqT9IOPrj9f5eL3XSr4Wx9NsoObcLOosr9I0uRWxlrSiZz7rJuZSILJV9tnI/34rI/HnigOI5cFe6FEHBt5WWKqgw+ZB42DLjvPnGav/1NrqwpU5I1xdVXt8dpc022zVLlUJHlkK9nU6M8P3xDlkMVgPlCP47l4w20Wg6991652x3im2/a1du6pEoe0esvUUNwIrLGs+fgOWyBltm5mZPNkaX1wqVr7rlxwe2WGR3juRwrjizDhikF3y0cfKPI4PT5bmVtv0mUPWIEMDbdjfyjj4r3K37uuZqClV3YpExbctfCcCv7PG39Q6C//eL77vi8IPgwW5jcLDz9zrIcKpmagNQMhmMNTJzAaGOBsK4Ukb/8khhxleVWNmsWJ+BjaydJF5gtrY/wKk6IWJfxzTeLjTlHjKgfFuIYePZZvXaYNi2p1zYhuZXxZBk5Uvw2e+hQ5fju2oSw0CwDlzGHssdS5ZBsPTr91UbMoE8/rf1eZGknc7/Gce3Ur1n6VjPiQgkxdy476sbXX9f3VxGpbDyjbFl5WGWWudlEVkZbbmXffivnHmZKGYroRoSUQxXAluXQGmsk26S6rreNJZcEVlzRrIzcHS3tVlbAg5+uhN/iOVz9/obaJ1qY7eqrAWgOSq1PE+Fid9AgYEN2vCktTCyILK2EVl4Z6JfGV19lleL9in/72/JXxpafMjLFDfjbjlg2GzhbtxKOW5krTN46roXhWGd9NePWuvHBUt846KDEiKtuIV1wErrVH388sNlmwLvvso+nl6+l4BFe5oTItK4ZM4DBg4EddxSnW3nl+mHh1luBrbbSc2neYYekXleT7ZAmpXlZBg4EfvMbfvq11wY23titTM2GyKLH1K0sS9sYIfGeSZcbbjAvY4UV1NLvumtyv6bh81jkx90qKs8JMS7jrl1yCbDllvUBofv3V++vRaj2Td99OX2Zmbqt8ihq5yWWUFuPEuVCyqEKwHu4S5u1Z9zKJk6E/9FFB5HMGk+Jb6b1BAB8MbWPpkCZ6sGJUpqLd6TU7Bz3nzrSvW8FuHgjWJQh6CDUynDOhdNIKtYr2ZSl3JaimEMO0Im3kuXLr+QEq5HfQUOmW0v/9FPugCMl24cfJp88q40QA1Kn6DZ/auGQbvWtwldfJZ9ff62eV2brdB187AjlghEjyq2vim0kIn8+IrcyGUzcygqMXpUp2PBWiyLZ0vGB5wWdltFo/ShkqtTWMnOddHeuNit2T1he9lgph2N8Hgx1IS4sj3nNQqCXl8jC69RF222m+RbEJP5MZNy45O65+ea6Q0aDz/zzG2Q2FMLm6zcTWutor6pgdFJ1K1OkcHAUtMn66xc8DEQPMccxhyLEmIi+7ir4khPUXNatrDXdmg+eim7d6tM7fXjlCxXFHPKEDUVizNOy2doFjXONfv61E6KoPpCkKUVB5ENWDuli0v95eadMSa5duiOdD2zfW198ARx+ePK/tGt5BSbGIVlYmRJF/BgitpVDd96ZxNdJ0Q1IXYU+kkUkL2+BWPSbTNlEPTvtZL/M5ZdPQuwVYeJWVuTYYNoP9t67vk6RPCqoyvbxx0ld//2vel2sl3wUI6ixIeVQBbBiFsybiaQqcqtbjEFs86tD6whi7FaWW+XV5FDdGUnZHLRgNM0qh1p3K7OhWLGhX3vjjYLzzR1kyR3kQ8CkcRRP6N1Jv0k33SufMiOBSlIUcyhE8pd80k+J4/2117LT6zazLeVQAJe5VPLnm8YmknF/qUpw5w8+UM8T0G1fSJDPCQ3efpv9u2nMoSJkAlL7RtYigpVOKkxgXFwOoUbZbThqVKung2Wy/cI0JGqVeOON5FNnF9Iy7iVXz6YqPftCgpRDAfD008Bhgm2shLsQga9AqMnHmolEkdAx3enD4LLLgCuvLE5Xhk1gWvb06e07l7mpSD5p64yONaCdgTNxG/5oSSYxxx6be5j8/vecPb8b2a0sh8WnTIyo/antAl7whQDcymwqh9Kydsd9tUOd5XFDNSD1iBHALrsw4tmkfmpZMo0lqxwKKSC1aR9K8//8M3DoofXHp09P4gOp4HNiGOIk+rjjgMcftyeLTUI2/3/kEfndhIpwrbTJK4dmzgR23z0Zcny2cZlWbqZuZaEsJH/+Gdh++2RvF1+4aIsXXijeiVGWIvnyx4vST5oE3HSTXFrXyChRS3SCAFDOM/Woo4BHH03+v+IKtbzkVmYHUg4FwDbbANddxz9euCOEzOKKt59pPlBtWRxzDHDkkcXp6ix97Dra1pSmOFtTdtPSiTnE4Gycgf0hb+mlMijm0156abK4beOxxzjbKDUhlp420ZivbRZXS/5J7jHmUB7t8xUU+gvmxWffdNEsuJg6mQsa7o47EuVqXWDpyy8XFm7LcijERSDvePZ3lqXPo4/yFRtFZfqe5KfYuB6m5yKrYKt4nH+r7LQT8K9/6eXlxRzS3YBV9lhaz8iRwP33t7u4lImvBVmjLAQfeCAJinzaab4lscsWWzAjWWih+rwpGmeyazFb/cjm2Oa7b5fxTL3iivaNKY46yqyskJ8rIUPKoQamZhDhKT7SRSJjxAniplJR+1oaNV/BRvh00oL8BI89lkQ+bQ36FCEGRo9mJp0yJZmYSZFtcEvKujffTHaEBwTNo9puDz1UZ+8rUtqZuMbFAG6/vX37zCCw/CrCdUymGkpWDslg3cqsxJhDNe04ahTw8styBRZ06PQy8ZRDHT5NAtG3pMqhOE5cg0VRWktm+HBg2DD+8aJL8+23te2tc7upBM/UKf/DD4G33rJXXp477hAvblxaY+g+gmbOZMtNJIhiDtkYrnhuZdm9K9LhIqugKuN62QwHqatAMy27TFTlcBEgPEXXxc8VLncryzJzZjIHNekTLq6L7/VZqDGHeMbyQGLJ/frrdutrVNT2CSaqC28m4stySBbT2UThyFNvf7wJXgFuBGJWnIo5cxLXKgAxegLYM/n9+uuZ5l/77guMHy8pSlYqSwGpBw9WSy814N94Y6JxKmGUfQy/w//9XzKoX3ih8+rksO1Wlv5fxoS25K3sG50axdbyy7f9WkjBeJsO18zLNGYMOj5wL4Az23/78EPgrP1b/Q/aXzP7XOSstZZYhiLZ1lqrfezUxfVbzlVXra2HhUndf/wj0Ce3oWZZiwLdvnPqqcDFFwO9eqm7AZrUWxVEW8ybvgOLovbjaUBq1m5lt98OnHhie0wuV/D6qqxix1bMoWZhwADfEoSH7m5cKWedBZx/vr09dmxR5jhZ5ZhDWVZeOfls9GeMDWiFUAEKfWplYg7x3MpaZyoz55agJ5w+ve6nGTMysatnz05MbVjYdivTRTSqTJ1a+z2K2rZYLspaV02rcqjsCY60jF9+6a7sDJPRC4D5IpELr4FlIkdbthxqFLeyadPaDWOy4al45zcVPdUqUBHWdcyhuh0JJSlwYRW6lU2fjo7I5U8b+ocfFAVpz/7TT1pZ21Btg2w/YZE/FZlLGUXAhAn1eVRlmzy52AhLtcysXCrkHys6et0yLYdShUNe7iJ0xiDdNvVVLuD2mZ69zqLYRpMmJZ+qw8X06cxpnJQ8uooenWshE3PIxg5SaTsCyXjmbcMJh9jeW8Y3qvffuHHJ5+TJ9mVxxdy5tX3TlDJiDrm6d5pRSWwDUg5VAOZDTnW2x3Nwb71z1v74VrXydJhvvrqfFlsso5Hfa6/kdWMWU7cy1Wh0OnWk9OxZV5fSZWK4lZXqcpSp38uAWhe1N8PPhitXVdZck3/M9FWUI5hi5Z/knCe7zeu9wALAppsm/2+9tb1yRdTI77C964qONRV7BT4lQuVQS0u9csiQpZYCevSwU5ZsW9xzD7D++nbKStO9/Taw0ELAkCG1v8taG6T07g3st5+ZbNnjL7yQyPXII8VyFFGWW1kV3q4utBA7trsJjz+elPvss3bLzWNiFSNzvC1oPUPJx5pWyVzv+eZzb0GRbYPbbkuuheoOfWW5lS2YiT6wwALAuusW53nlFXV5fC5w11mHf6wKY0Qe3QDWocXvE53HySfX9s2isnSw3SdXX91OOVXskyFCyqEGgGdNU3OT8GYira8hP5yxrAPJiqkxFHrggfoEAat9Va2YCgctBzGHlOq3WZeOhZfgnOPprB3SHJIN0MDDQoPatIQTilOicghIYl0VYbM/csc6xzGHtIuXdCurUw7FMdDSIq00lm3j77+XS2eb4cPb/7fRH9JFZBoz3+Qt5733msuT1v3OO8n///ufnTLLwJenuWo/SGPq2SL1luZtRV8GuvdC1q2M1edtxjSyAass1m8ff+xWjhTT9pFRYonisBHuKbrG1p7xjpDp96ylFA/VyB0hKF88Go83DaQcCp1VVgEmWrBxzsz0VnrgLHyBpZIvtmKP/PWvNXtT/hPHYxs8CSDZbWHppYGZ6KxfvsCt7MS552FP3C3MV8fzz7UeF6TJceedwCpr1rvfcRdqF1+MmDWrkaHtetl/Mh1yCHD4oYKtUiwjbf0k6ovTpgJ9+xqtYpdcMme6eu65SvmPPx546+kp6k+bgidZswSkjj8fDTz1FNC/f3IhOK/mR48G+mAi81gdc+YAyy0HfN/ud3jOjQtnKnXrVqZb/KWf7VBz3XfCQzjrf1u2fRdaDs2dWzcOWjvNww5TSj59OrDEEolljCmsc1DdqYR3fcqc4D/4ILDiiu3GuiNHJvFdbOEiXNiLLybXMesypNOnHn8cuOsuPRl0r5GJEuvDD+sfK+l5n3pqEkrQJbfdBqy2GnD22cnuPDYsh1LSslRCMq62WiKTKQsuCHzyiT8Fjqje9dfnX9eyF5JRJHZXCn1hG4LixPJ0TIl112XvrFkGovtb1yoRAHbdFTjzTP5x1jP1z38W1y0ixD5+yinJ+S2+OHDRRb6l8QMph0Lno48Qjf5cmERqcZm5Az+esjiGYlBrZksj5VVX1exN+Xf8E09jGwCJ3ujLL4ExWEK9XAn5Lmw5HvdiT+ejzH77AR+NaL9lXMY/ilvcncuNNwLX3uA2xhSrTxZeHkGC6JNPkh3SnnpKXITgmnzzTS5U0mmnSfWvbJnnnvxLobx1Mkj2S6cxh1J87lb2wvPJav/rr5O/k05iJrvlFuBH9GEeA1Ar7KRJwGefIfrow7af7n6qty2J67AVc+jYjw+u+f4IdsKZ//tt23dhQGqF1bByn2IE1Rfx8cfJrmJ//7v5LkOs36+6qjhflryFl65bmQn/93/JwpgVm8XGfaZTRtG5nXBCch2z7zJ0lC5HHKGexxQT5dCllyaPlSeeYB/PLnp0EVki7L9/Ym1yxhnAo4/a6YMyfZ5nsfPBB4lMpkyaJFasuVbaivrEG28k1zUExQYAvPZacRoXsoa4KFehrN3KUljt9fbbwKGH6pVniqvr9+CDSRDuLEUxh1wr0VWw0S7/+Efy+d13ybOxGSHlUEn8+msS8d7bTsO5O6Zt8S47Un7wQdvW7aqkgRHn6GyO59PhN4POVur3YA+MxECt+uIouTWjqITzzrStbjPXWTJYVpzFHRITiqFf1ioNPsMyNd8vxN+FE8O6xbbECV+KYzlCxckqUOJV6zDwYxixAlLHMXD55cD3WKiwbCkKAlKLsNYDi9xckShFLrhAocxUiyIxjolCWqXMnt0+MWBR1jCUXiZmfQpuZVn+/W9g1ChxmiHYu+b7ffcB775bXPbQoUlAVkDcRq+9lliXsHDRtmk7yjzmVIPz8ryA037GsvqycY4uTPxl3XuK6NJFPc8nnyS7Z+nWWxDbve2eZgU89TmtcFF3HIsVL3mlqS5XX53sSPfjj2JZePddoGH7vBDCuYUgQ1mMHq2W3qcSUXs+nsv3v//Zv8a27m0dROfy1VdJuJKXX06+f/ON2/oaFVIOlcSFFyZBwnQ0rMyFtqHtXtviQnaRuNpqScBoDdItVY2UQ62zb+tKB8lmvPzyTJ62T7Ese6FWmaYUcyi1SJATTwql+oso6YmZ76drn1W7N/IaqF25XoZjuItPwEI8cqC2IVdaqe5VK2vhPgjsQAMxImb6ESOAo48G9oSeQra9Ao7lkELModewoZkMqSj5ihgd8u67FS0BUt8diQubvYd53HJLYlKch6ujTt1dLU8euAveKNI2lTjwwPbt5XnsiyE13//wB3Fs9iw33licZqON+MohE3jWGSoWCvvswy5Lhuw5pcohWzF7bOypoOqKBOjJ31nDc3zFFRNrK1duZTfckNzTskpnnwsBmwGpWW4nts7tL39J3KxvuklOFtnfbWESs4lgU9UFsonc+Rd2IaHSVzfMTOF0+rjKbmUhtNNf/2q++2qzQ8qhkki3g5zhIK7uxzLWKbkZVAcUWxCkW9Kakr5BFSmH4hj4ECu3f0mxHXlM1kE3B8tFYDwWSYrUtK1IXcc+/xz45ZfcMckiP8Aq9gbjOC5s7mlQ36qE1T4zZgCffVZfvyrTUb8DnmhLzLrzM5kNTpqEr1v6YWLOBcqGAjNdYE5R3Ob9s89yfSn/JE+/K7iVzULnZIuVz8XurYXE+a/1lYrGxw+xcn1PSiOASuyDmr/HWOS337YVYwiTJyd74n74IVrmFhfCVQ7FsVZA6vTT5rbEU6cm3oG2sGlVo+NWZrJVMUtBIatcieMk9o3oeBbWuXzwgf1JeVmWQ6bw2jl1k0qf3UVbsBddB9u4UEQUBaSWtRyqezZbIGtJZGNbcNE5yPRd1rNGJp/Mc0SFEBbTNmVQGYtcnPv06erWQSICcV5QwvTeyKOiHFJFJA9vzC6qOz+PM6VK194WpBwqGa0bs2AhcA5OV664TTkkuMvWXru4WBlSt7K5YEVXTbjzTmBVfIjHsIM9G3cWeQsqDbetdFG7CtS2Sak7hUsvBQAsuyyw006ouRYyCobHsR1WwwdWAkhyZcyxNt6xUvZ++yVxhG1PtPL15LE6If/mG/TH1+grGzy5ANZbKhXFYxqbeY/tGK9MDNzKIsTAJpskHdUEg3v4GfwWq+JD3IyD2s9l6FDgd78zkynHHM047YXjyLbbAIstBqy6Ki7cfWhheUJXmSI/GgYudp5abTVg993tlSdrESE6lr+/iyayLGWZLaWIaAvxLNdeC6y6avsOa0Xky/jvf5Nrcccd/Dw656TTZ1SVQ199pV5HHp6cN9+ctMvTTxeXEUWJZeGqq8rFgBEhq1C25TJSdExkOcSzjFhuOa1hphAb56xjXZW6v2V/V9nRKcsf/qCXLzRcWMU8/HByz93N2R+mDHbaCVhmmcJkUkSRX+UQa2yTeVlQNA6YUNQONufYO+5oryxCDVIONQumbmUGyMQcSg0APsEKtSNibqRRtsooXHGoFWdGrrJ32hUtzz4LtluZYNE5EisCsPi2M1M/b4AfheWtVPX888mnfCwnOxfKSnc3dOmsOcRxK9NRDqVvWV56uxs/UUtLskFf7j4qxU8cglVKAZ+29rv3sVr7jzbNVlrJx4QrXOi1zRzl63j7fYNdGwGtgNS6Si8RtpvfVQwWgN+/bVnyyiqHWOc4YkTtZxH5xf7Ikcn/Js8BW+9jVN3KJjL06qr18pQY772XfMpawaRbjNdsWuCAslyXNI2ktdKGRl72dFwvUnjJXBvOJptOqNo1+Kj1fansWGaDfBs99xz7d53yqtb+Imy/X7flVia659J1gm8aqR/IQsqhkqm7EdJAB67RsByyhYxyqEa87Jc0OqzM3enQrawmua1IQIKZSnzFFdLFXHopcO+9xelEp3keTkY0fJhUWhlYbZRt+lRJU7OAylX6KjbE/+F2qCJrOTQcayiX7RLm2x4FrUO60O3eJdOvGNqN7fAEOrxbGwNJNAzouk2a+rZncbkzYBbehgGpm9+11+YO7LZr8lnm7IGhdShqn+xQYxC2yAlRBFx2mV4TLrEEcNBBxRPV/PGxY2uPZ/+3ZTkkW063Vl0uz4py79oY4VzrKBuYxhwqshyaMyep48or6+tzFXNIhr//vfwFgAvLIZZbmW7MIRftYXOnKJX0adxLG9ZQplPmY46p/Z7KOmgQ31Li1lvN6izC1rU+7bTkkxWMX1Rv0UYJLIqug8l4rspll7mvI+Xoo83yu445JOLqq5N82XlW3ipw/PjicjbaSH0nyWZU8JhAyqGSqeugd91lqaACNGIO2UJlt7IIsfA1pvIi0YHyS3ehWrdOn1s8q5Wt6eKL1eXJcirOU8ug0a4s03DRxP5W7J/Ja+c6ZsV+ALtaKdMU3m5lqqQKjHm7MpRDmQZ/CtvWy1CK7sX86RwhdiosTzk0ZUry+eijvJxuZh7MfqBhOZRfFLncNVOn7550kp5b2bffJkHEi1x38l3m7bf5aWXlz5bJspiRvUzduyefsi62Oo9snWuik6dIttS68dRT1cvmwVvwyyxi0jSqO9W5xkRxJON2wqrH9eLJtquLinsdbxzUwdWUedgw0fOlWsgqh1Keeca+DLpKY94GAKL+dvLJenUVwaozXTLK3tcqx3jYCmqfbvYhin0oszsqIN7cyfZY1oyKJVIOeeDpp/kd++23gYsuclApz60sijAWizmosB3lrexTWbPbi7DuzvvuKzaZKbyra4+/hE2K5cuha1mBuXMRv/Em85CqAsr2elm6vH/8Axg4sN5NKdMmrPZJJ1guXUlYhLYbSd6t7PvvgaOOymzCpdG3unetnxWd+cEuicm3KHqrJDNnJrtBiLYx5taRmWmVZQ2U8sILxWny7lfSCgOFCKvGik6NWa+L+CE8ylJEsOBZ1fAWdNl6P/1Uv14T5dC88yafssqh/ETdxg5XLHQWV6rKBhlD3rlzE2uL1NorT5GcWSXRL78ARxzB3snGVWwRlfKKruVllxXHRMr3+WyZ+dg7V11VXI4uLEWVjTpM+nsIlkN54pi/k2ZZi1Jbhvkpqgo02fRPPJG8DJBBVzmkolx1TYhKCVnLIaE1uoc2DW3uHzoae4sTpmyzTfJ56KH1x9Zd11GluZEyqxw6AP92VGmCsnIolfWkk9p/Y42SbZEB9+CnKSC/UNsMLxXnsWQ5hLlzgfUHA6gfbdvrkDsno4HPxquAfJE1bcRXDoncynQRTQrq2knVpTBWzyObPo6TLYLvvx+Yr34TNmnm6Vi7QvsF3XDWBzvjsg2AqU+yZYmm81/l5BVUd96ZLCpmzRK/valDc7GYZBV3cBkl2iuvFNfPs6gpvIQ2twErYu5cZaUhT+kVCqYLSVXLId16eLDKl12cpC4vqeWfal02rqXAWNcJKtfltdcSpcgHH7THE8misgi86irgmmuAnj2B83LGsrbOt6gc3Tf+QL1LkqgM1Xp41jYhYOuRG6rlkKmrkA42ri+vj6m6lcmm33775PPAA4vT2nA3LdOyrqh+W/lMlSQmbmU+Mbl+IY2FZUGWQyXRdiNNmND2m0mHS90cpOFZDkFBaZPy6adKe0VqWw6xDjmwNnj2pXkwW6ENrMmQ3xqcMdrKDsDZdG++qWjV8eKL7f+X5KjNVA6lkVWZaMjDeBUv257c2mb8itnvfYRnsaW6PBKki/i0XfJKgPewWqENdj6IeepCOnMmuDOmqJO8HXhaRMvUnwquWS35++aZGRtK530H7VsnjpvdB8OHA8O/6IlxWIRZNo+ffkrinfCsEPLKIVkT56/RH5/B0hYpRSjMet96C5g0qVzLIR1klEMyyO5W5uLtfx7ZgNQpMjLNmsVPp3JO+Y0A0n4uijkUx8BTT7HPYfJk4I03an8bO7Z9owmejDLuCqkcvD6s4laWpg0p5haLd98Fxo0rt07XyiHbbmWqdQHlKod+/TWcgLpF2FZCqLqVqfaD22/nz3HzIQtM+3FanqtraaIsLgvWc6Homs2Zk8QYyi5zyn4JYYusjNnlUiNDyqGyubjdZ0x+p6Z6zjhb8dLxnsw6d+YKKyjtFWlFOeRoBHl13DLYas/eOANnWSlPyQ9+1CjuolZmsZtNkx2oBw8GNt1UQbYTTmj/P91uYtgwdlotorr6mTGHBg3iF6Fw+dvqOfjgekmklUOchJ+MxGmHT8JWeBavYQO5wgSVFu1WlmcNvAdsvbVWdS0t4CuHOvBl5FmpxPfeCwwcKJQll6NNuGdfnxf7TbhMOucQ7Nv2//IfPYC11gLWOmELLAe1SJZ77w0ceSTQrx/7eN7CZu212elSsv1kOUhui2QKKyA1p79svz2wwQbNoxxilQvwlUYuHzUuFsMnnaRXRl6W++9XzzNkCLDttmxrwd/+Flh//drfTjwx2dZahIX9IYJX9KiQtscbbyTbyevkl3GL8WU5ZDMgtY4lYZluZX/6E7DllsXvUauwOFZF1XJI1Rrr//6vfRrE6+82xoUoai/vgQf46VxZpdhyzVUtU1SHbP6zz04s4Zddtj6va6sjl/fU5pvXv9dvREg55BGTDjxqlOLdJXi6qrootCjmSB8UeeXQXF73K/Fp+cOM+QEAoyA/E8srDqxZErEshzTjGWlva/xLEjE0njDB2qQ7Qn1BbTGHJIJyt6aUrq+t++RfW8POQ+kTrAAAmIC+igJxDjOUZ20PUY3rX3OOcXtsH6FyyFFA5RoyVXz3g55Hc4QYP7W0+9z9jPnbfpch3d46dIRdhnMNYwAtjHhGn35avygqw2WorHyyZWabrQyFgos6Ro2yExyUtYV8nrz8Y8Ykn19/XZ9W912Cyrm4WPCHvDDX8VSVVbL6cpExrVf3/NLvZVoOvfqqXDpfys1svUVuuaqoKntULY1E2FQOhTI+sPq9LYujop0+ef8XzaVFMfyq7pI2bZofOcqElEPNgsUnUG/8iGUV3pLzLIc6ocAenPGbUBGjpWZvL/d27FecnyHDFPSSyqdk/eIpIHXcMQmA8dfHt7X6wAZqL0+bW1n/pfjps20waZJ6PQzTvJqtk3k74+Xr5sC1qFG8drdldmVL+ec/lYrgk1cOcc4374omg6pCKZu6xdOkS3cByv3dcVBtZr2csfwMnIWOV1/BPBa65dDcufrXRpQ2/XzlldoFyG67Jd6ZLi2HVN3KZIhjvbE+X6/MdEBHVtU8NhRdsjH2be24U4SsBY4rGWTKZQVALsNyyJQrrpBTbAJs+cu0HEojSLCC1WfZfXf+MZfXICsXr5499tArWzR3fPnl+t9sxnGyqRxSrdN23vS69OkDLMWfKlurL8tOO7X/v//+wJdfJv/LupXpPluroDgKcWy0DSmHmoVcb47Rbi+pusibip4YrRBfoy3gJvhPyRrxbM6qFfLdjb306tCrTlyOr93K5nERo75euDblkIMhqO0aMF69GruVFRxTJVsW0w3FouVQHHMqSQThl2dJCZYtJg7UFSQU5ZCwn8Yx85pcg8O5WfLuco1GkfsYiyeesPeoYeUJyd1JVjlUFHNItR6ddLxjQndZAb4XG3mFhEt5al5+tP6ve01szGXyFgembmW33qpeb/a7rHJIdO6yigzZ3QdD5r77xMd1AlJnQybqupWJSMtKr7VpP7bpCqlbxuTJbItN23Vn8z36aO2xESNq0xRZHImUQ1VzK6uC4tw2pBwqiweKnfy5kyIbLh+CV0Uq5R/5xylt/0+fnsQa+KzAiCi1HJqNedqr5qTlWnNo3I1X43CcNeskYZq2CRSi+oVeFLWry7Oi6O5WxiinpixLAam59cto7WWVQ7kKha52LbUzsvPOA776Kvl/K/CDK+v2+7bzZJyw9ESZc42/w2J4FDsC4MtX8/t11wGnny5Xp6UHTr1tnGDFl+aJgJnojO3wOD7AKnYEqaP9BGUWp8Gw+eaIua5c4Qgsul9suZW1tCRvumV2flPF5oRLZmFsw81FBKvLmLoC5PP//DPw97+ryQXILZJVlTSAuqKGZc2jqqC1rYRztbCYOhXYYgu1RZ5uXfn/VfPq5C+ipQXYZRfzMlJE8rW0sPuWj93KbLXjJZcAF19sp6w8339fnEblPFSszo86Crj3Xv7xb79N4rxMnixXnm3LId24aDYU5bqIyrz55trvH34IDBiQbHYgarP8+KxjOZRSlhWnK6oosyqkHCqLL74AoLeYcLoAUezlV97Rs+3/p55KtpY98URxnvRBkVUOzYXg6WHJrewvuBpnzmZvtZ6SLqi45TIW9vpb2cvnMwlIbUIc2R8S8ovWU09t//9zLAsZyl6E8+p7D2uo5T3sMCWXuDw2FMNSyiHEGI418SS2w6G4vu6YDdmy/Z8VGydYXnyRu8+4q36pM/nowIjtlWJLOTRtWhLM+He/46cpwciTmzevFCoq04Xrl+1y8mVmy33iCb1yZBZOKosrVSuVfD5R3qJnW9FuZbK4dvO6/37ghReAq65yU0++Ptl7IJ+P912H7LUbP15OMSZ6N/jTT2ZyyJ6TqM+52MpehuOOA44/3k3Z11xjtzyVNrriCuDxx/nHzz8/eQTfdZda3brKoZDG/7KUEF98Abz/vlh5mspS5FaWT6dKiC8IyXKIcAZrITX5uWG4B+1OvWV2OBO3srqyCrKzLIfy3HJLbYFjxgCPYzsjuWSwPQ6J26KgoTKZ00Wn7LWJJusrIGr4SSMKJgPrSg2VfO8MlXsVBji96UaNAl7AZuLqHSgXook/ZCqIpZRDOu2gJXuUxj/iKJwYRT6A9tfN/8YB6nVaoPTJwJzZ7N/vvJMbqVbFckgXl2bhRW2scw1EE9RXXimewH7yCf9Ytg1YC5cixdPIkeK6XdLS0va+qoZbbpHbAnrUKPbWzkWWWrfdVvubzFv59PukSWw3Fx23MlH/LWtxwmorG3Xr3qOia/foo8B336mVN3w48OabanlskVei2hq/H3ssUXI1GjYUXtnxjGU59J//JA4M2ceXDQuc/PGs5dCMGfKuiL7QcbEFxMqZceP05JBRDsleD9Z4Yao4ylOzbiyRZlAOuQgwQjBgLaT2/v1PeBn3WK1nlNrOzkbITj54u5VlmTIl86WlBWusAUzC4+3t5vhuVFno2rIc4m1jrkM04iMAm5gXNPQdAAX7d0vg090mvvpq4NXhRjNtk4DUKcsvDwAvSLcFc7GgE3No0kQgExNMSjmkUr52s2bdyuQL2Q3te8j+hB66lUuhbG2gZQkqweTJABaq/330aODwwwEcUldmGZZDKT6UQ7bLfO89sZUCAKy4opxc//wnsN9+wMort/9WdKsNHKh3zjqL3ny6uXMTV4I8V10FrLEGcOCBYvmTsa1WEVHkpvfyy8Bf/8qXu+hcPvoI+MMf6hc+Ovpu0ULHtB/m86sMuS6sDUwt+eIY2HFHYOml1fK/9pqeHDYVZPnvpq4+v/+9OF+3bnLlh4YN5dDAge3/55VDY8fqB7dWVXZmlUMnncSOqKGC65hDusqhlLx8IiusIkzcjWWPA8Add8jJU8RBB+nLoAJZDhHOmYkubf9/jSVrjnHNqhUWiDNmqMvkehtr5gSswD2szhNHw60sz6/f/YjJM7vX/NYhSmZu3MDIiqOAquUQ73yUA1JLXMN4upsIiT+iV02/5vLLL0pRGrVjDiFqD2qUP5Yp8kf0Zm1o1l5G2UhGDf6epTTgMWVK7blwOqjwTXruOmgvNgqCb5swHovYLVAS1/2kRmmeMnVq3U/jp80rvF/mzKi1RPLh+mWKzkRZx61M1ugwT/7Zy7hM1he+2f+nT2ennzkT+PHH2t9ECos0toep4ioPa/vf7HNe1q1sds6oLvSd+FQVE7bq431XzZd+sizNTORSScf6be5c4Icf6n9P09tUYM+eLbdLWteubFl+/DHxTJZpg+w0oKw+I6McysoyfrxYtqlTazeL/fXX4jKLUFUOffedOyuvvNys8/P1rGQ9d2Qpshz6/vv2NEVuZaJj2fhRoSlaeGNKs0HKoZJIJ+5n4Oy237xaV2Tj1Th2K2PVyUO0vbgpmy3+KXr/+LlxOc6uG+O8pR+IMtdwm60VBZKjD37EdTisMF38l7+0v3KWQD/wt5y12bU4HFvv21e7btn75h0MkkoXX13v+M+qYxEorF6PO05KORSXtbd8m1uZvSLvwR4Yxmvj3PnqvvWypcRVZb312L/n+8VNb6yE77A4t5y5x55gRZ60fUSLCR+KJ+51U7zeY8bIW0jk4/3kyzr0ULlyVBCdDy9uyPbbAw88UPubTOBRld3KdDZEmDED2Hjj4nR58n0vpF3hWLhYANuqT6YcXTc115x2GrDwwuxjPLcyXeulQw4B+rKnCjXwtq3v0yfZGlwmts/RRxensY3KtR0xAlh0UfG5/O1vyWY1KT62lV9nHXGga9Xyisi72ZZlOWSLOBZfp6+/BhZZBDj33OS7iXLIV9yuIiZOZI8pZDlElErdDk8V63DS24IrnFeMSPjqyGQx9iYG1/2WllaGW1mhLiFz3i4sh2RwEgMnK9u332qVoXV9JDrey2+yLZ5stsNHWJl7rMaaJt0rNIOWW1ne0sfQrSxfno1Fgs2A1C9hU/7BgNxRfTL3jbdqvpsqcEJzK9NVDuVvhzFj9GWQwbbbkEx5rNhAJos13R3X8sfyRqS6C/eigNSs7d1FuHJJ8KVcCdlKUDUeVJb8Vts2yZ+7rJKBpxwCgCefTDZwKeL+4o2NraPSN9PQFc8+K0736qvt/5uMN6r90OZ9pmKBNnx4fV7ZOnSO2cyTIrIcSqfuY8cmnzrKoZRQt7LnxVUj5RBRKmYLC3uvh4oWoEOHAj0xGROwoG4Vyafs+XICrkpXpEAU8RUy/8X2WOzBK7kmsaoi5A/VbWWfOwYAE6bPKyjPnvWXMmPH4rGDH8GiUIxSWSaMJ5CPQd3k2vwPGyrnF6a3EJA6m3Q1vCe900mMCCt89ihuxoFWt7LnuoQCyheclXw+/ISpM+qViGfgTKWy25DdkzfHVPRAb0zCy2CYWxSQ3x1S9z545pnkU8E7VJp99xUfl5lE29itrExefFGtz9uaVNt0xRo9ur08EyuZNO/IkWqBRm1dQ9ndkFSxuAdAIWnsJ5v12LZE0vktJdtnRJuAstrg4YeB5ZYTisi9v2Tvu86dgZ13TnYWS+nfX72clJDcylJUla2AnXtA1a2sbKIoeS4utFCyk7Pra2e7L514olxA6qL633+/uAzX1+ipp/TyyYZlIeUQ4RRbE/Ys0tY8Coqpiy8GpqInnscWWjIxJwCi+llbxHiwADgKl2PcjF51xi5lWA65wLaVw99uXgnjsajVMm1g4zxtupWVrbizbTlUW1ht2R9gNRxxhHz2T2ctjYNxs9WublM5xGI65mP+fjbO0OtrzEBCxQzFIExGb5yFM5Tz5jcD0G2WE1q903Ri2xXxxhv2yyyi7DeC+efz6acrWtbGdmS0qRS7/fb2/2Xfghe9kecFGgXq8xWdi2hB4uJ68/pUGQsKm3XYditzYRnIg9Un0mDoOvXInvs88yRKqEsukUsfCq7dfFxaDskqLXRQsY6JIuCzz4AJE4C//708y6G8jLrn/9JLdpRDMoTqVsZ76UWWQ0Sp1C1qGJHE3sEgvIH1ncpRtAXyPK070M9Czma2NXKn9CBoEnNo6FDg7rsLy4gv+xfOx4mYhN5SMqXnzio33f2nbmDQjoVTlEDNrew9rN72vw+3MtHuSNly7sGeymXjyivl0wrqdlnG1ZDTioiuTRluSarKoTcxGNMwf9v3GvnjmD0pUHxacsXQ8BUQ7YLoGtXr9xB2whsM91YZ0uvwGjbEOThNKW/+RYQI1pU855wk4LHMZT7vPHbwYdt88AH/mO4b7Rdf1JdHhnxg6CyPPlrrisFD1a2MhUzMIR1M8srqTPPX9vXX611xZs8GbryRnV6Vl14yy1/2QsLWQsa2cuimm+p/k3UrK7sNUxlSKzYVyyFTvv8+CUp9333A22+blyeDzmJd5Zp8/bV6+fl6ZK+BTcWDiltZNp2KEt+2WxkPmfMQKYdsPhuFG6B4sPz6178Sl3JZi+hmUA7RVvYeqYs5dMyxAGqfoOvgHedyiBavjz/erhzKL8Ki008DcENh+co3EivD88+3Bk8QKH2+/hpvHPMfnIzXtRdgWdJ2sbYVrWXLoXuwV9v/oSmHssxA9+JEeY48EsDN6vmQOQdGe9pUZD6FbaXKkr02thRFQsshyeAsx4Lz6pOXf/hwYK21ZEXkv6F96AEABfsF53gGW6lXZAnVa7YLHjIudzY6Y3ZeUV+AipXqKwy3tdNPTxQ+MmPhL78Axx6rJJ4UeZlXW63+WN6trIj8+ZympnNTJh+7JCvnF18kAZrLmHja2LI4Jb8o0i3vyCOLy2fx/vvA7rvXln9z5tFhutjYbDOz/GXHHLLtBmarvA8/VKvXBFMFWZo+3Z59/vn5abPkt3DP8957cuXceSdwwAFyaW2g49qqogDZYQf271VwK/O5lb3LelmInu/vvqtfbkoqW2iWQ3/7W+JWfPLJ7ONkOUTYpaAH5RcAH4/rJV20sYmv5KKmQwegU6tOaDbmqT34009SZbT8rBigQtMGddq0doXF9+BsY5EjbQVW/J+0rLyXm/4ivn7RXuZW9rZRrbOsc1IJSF1YhoW8ISmH4rnseyu/W9lbWJddHq9Ns/vvSmDTrWUm2EHFASj3gTTYoiyfYxm1DAaY3OMi5VDeAqfOQrQV3lbpLLJh4776Sj6fCJWYQzbKDBVZmb/9lr8tr47lkIzLjYpyKH+teFsw67gzuHB71EWlPW0H07WZ3/duZV9+KR+uzXYsMVvnPnu2XDqVOJdTpyaKZRN0Yw4VMX068Omn6n2Rdf/KKtZMFQ+69w0vhppJfS7cREXYjEUnQvaZYZOiemzFlm0USDnkkoIIh/nF4BrPXSRddGzr4ReLA1J3795uOVSnHGphu13Vce89SbrsIlW0EBYUKMq3/qErozsSRdQvktYqaUBqVrlpu+y0U86UX9etrGiHJkW3siwyC0fbLkyylkO6GG9lHwhlu5WJlEOyk+YPsSr7gOrKkSdHSQ/XV6auVvO9SEyZ3WSy7IyHpdLZCCpuUzmERx5p+3e11SCF7oRoqaX08uWRUWjYiE2iS1kxZWTdypZYgr/Vdwi7lcnuOqZSfwiYxBwKwWqmqDwfxDGw9NLasfyV0Y3j4qOt1loLGDDArAyZ82O5Chfl23lnYIUV+Md57bX33vVprr1WXJesTKroWiy5uJd15FDBtXIoVMshIFnrGkxvGw5nlyiKomWiKLo+iqIPoiiaG0XRS67qChZWYOUMwkCqmtgeGEXKoUhSQ6WyAI4Qa89cR3zRHV2RqH9llUMissoPOyaiBYUEMAK5cCtLKd26ybFbGQ8fVlwiapQyc+TiRGWRshySIDvetbTYG6hE7f3lr2EETPetsKwLSD3qM61yZIfm0La6L7PMMtGV39UiQOU5KascstWXfCmVyt4RT9fFkldOFe8RW25lKWX3QZX6Ro/WkyWLzGJ95sza7zJtWrTdPY8XXlCrJ4tpf5Vpe5uKXl3LIdU+aRpzyAZlu9hmKaqzu8KSsYpjoiou9XcrAdgOwKcARjmsJ1zmmUd4WOgOUcBTz8oHGGUhuw16ly7FlkMiIsRti0LpBRLnzlsMY7EPhhTWBzCUQ5wyRQGps+1iy3LoIhxXnFCjDqaiRnIE3gQvadXr2q1MFxO3sglYsLYM3fozdYfkVtYyly1L3q0sz0Z4BRvi1ULfEuFER8OCSQZRu6WWgVWjaDxSxcbOmKYTouHDzfLL1K866fS9lT2gJvOTTwJbbmlep0nMofPOq/1uK+aQqeXQ2msDm28uTpOXhSWT7Ym/Sh+zsWiypdTJl+PTYstUQRDHqNt1lgXvHGUtHYrk9LWovPXW5Nx++glYeWVg21y4xG7d5Msqox+U3U5RBJx5plndUaTnLmUyZs6cWb/JgauYQzbxEZC6qF26dePXTTGH7PJYHMdLxHG8O4ARDusJl4xyiDWx/xmSUe4swOzLrXeCaNERRfyA1LK+bcoWUpwRahwWwzPYWpg1XVTV1clVDtXmqz3Wnicrkski/lScW1MOL1hwmTGHXsEmWvlcu5VlUWkPE+XQCKykXB+TzEqnim5leV7DRvgfNiy0u5VVDpVGAz3ErbqVadwbeZcmVR5+WD9vEbpyhaAcUiWrRAnNmsqF5VAe3gR+6FD3u83pULZyxdZCpsoLINvnbOva+WrTCy9MPr/9FhgxAnjqqdrji2oY2Ppym3JV5llnsX8POSD1hAnyaW3HHDJ5wZRVtoYyzhQFk88SiswucaYcimNrUXGqS6fAN4OT6OFTpwLnn5/8z7McevRR4Ab8iV9N66LQRswhWerKLyhTtJW9ikhGi2Pedi0SVCHm0H/wB+Hxr7CkiThtxIjw9tw1cd4vf6s/VvRmDxEuwN/xOtY3kmHsmPa2KdtySFQuz3IIr7zCzV/nVjZpEr8uyf7Ps+jRUYAILYcK+uhDDylXV0nqLIc8KOpM4wzIxBxqhoDUoWErILWrmEOu02f54IP6He94ljyu+p4tZZSqBdKTT6rX4ctKoIi8XIJHntV6XcPbcEHFKrNsCzLXbXrffeLjvPMdNkycrgzlGWuTiEs4G83KoKIcmjULOPxwNQVVSuhx4/I0o+VQ4NqLilPgVlYm+cVA1rpCtCBLFUNA/QIjm+9Q3IBDcKNU3cXC6t953LoklEP5drDtVlZ4XpkZiK/dymzFHGKV86xoy3EAe+Be4XFZYkRYdzrnFXIcA4JzjBHhJFxgLMNBh7TfK0Eph3gxh4bcKd+Hbr4ZwAm18ko87X0oJKKCe26XXUoSxAIm93jeklL3rZ9sPlZ3cLmLTAiWQ9lFeRlvjU1w5VYoe41YbVTWTjlFmFyTwYOBX3Kbs+qWp/ImW4Qt97KiIX677fTLtpVON72t/KFbIh58MPt3H27GvsrO8wfxO0tmv49jYNAgcb4y3MryYw3A39XNdsyh++5Ldt9T2cVURRYfkFtZOwHGDG8gfFgO5dXZAmJE+O+L8woXbdnBwnRrcenFoeyelUpCsGWPa/6vlS97vs88U5t/ONao+f4z5i0U4cmvVqyxvhJtZe+KojdgLmMOFZFVPv4P62MS+miV8xwEQTkKtly3dT3mfjuu7f+g3Mp4lkMCWeoshziBOr77Dnj7bb5cWQWFTbvS0AKAs5iIBfE6BhuVYfU8NQIAxTEwcaJ+laYTQl+WQ2+9pVZmqBNfV+hYDrHS8RYm777LL8cFJuWztirXVRistJK+HFlMXaxCiDmkikncGBPGjzfL7xrePfbBB3L5v/4aeP99e/KYKpzzx1W2I5dBpR/lx0HXLwiiiK0cMqlHRTmUjmsq4xvLrSwUyrhmVSIIy6Eoig4BcAgA/OY3v/EsjUVsvfpR4YD9AXxY9zNr4Xfr/5bFgf9eAsAS3OJs3ASsmEPCRfEFFwA4n39cgKrlUEvcrrjKL76yljHvvlvrErcWahdX++NW3I/dhe315vj+Aslz4jqyHNpiC6VihejEHJJdIG+I/ymXnfIoduQfnDMHyLtHZrCmrPlkJIB+AMpXXoiUQ3NNdwmLY67h1XLL6b1FymLbrSx/Q/p6qO+Ax/1U3Eqd5ejzz3PT2uivZVsO6aaVmdiut558vUC5i2gX8WRU3YlsyCPib/XewYWwrkE+YCzAltfk+nH05lrlmsY68WU9EwK+4iwVKc9DbdNzzwXOOac4Xf/+zkUx4qij7Lexyg5zZQektq0M01H0qFBFRTMQ7n3rkiD0d3Ec3xDH8aA4jgf17dvXtzhWiGNg5uzym1fFhWXMj8XWLqKbQrYu5YDUFqhbMLJe6WXSsc6Fd36sxejHGIiYXw0XXtu4UA7FiKy+9dFRDk0A//4uw4oqniW+QGVY8ojqtvHQFFoOzWa/GlLZSZCXskgxVBtzSK46GTqC/7orahG8CiuwIgsNE6UN162YQd3GA2kegwlSHJu7DblwKwsR1WeIDqrWLDNnsttYdzERgjtdGe0MJLE58nWzvgN2dysT1SNbThy7badQYw65IlS5ZMhfqzlzzN3obLfHqID2xS5DOaSCzH1cVv8UBaR2NSbI9FXZuqt8H8sShHKoEbn6aqDrUYf6FqMN5mLgm29KrVtmAWr61ppbx3zzMX9OLYdUlEMsIsQ4DedggQWkswAA9sZdahkE9dvAtVvZE9AITACLSpuS3MpkqVu0O37otBx/gnKevFtZHOk9NlwpiTuBf02jMV/zMzqICfcA3AUxMrnHT0Ht/uOifr4tnmL+btI3r78eOOMM/fym9VepzM6d7cgh4qab+MdY8nftClxxRf3vm25aO6EWTcDzi5OyrDtYE/6PP07a+YEH9GTgwZItv9W0LKaWQ2l+k3Y+6iigTx8zeWxQ1mKsahYNPnnmmWQzGheoKJKzY45KvDeZunlMnlz/m4++o3KuRxxhtzwdfLqVHXOMft58u4QeW8wGpBxyxJAhviUoJv7qq+I0FgYLH4FoZeuMM8qhfB6eZQxv2/ubwIn2J5DxEewkXYcI2YVj0QPMVkBqHk9iW+U8Vil4feLbcshFfTWWQ6ZDvsGA4Goc0OmHrniYcz+rYrutvhW4DpfBXRZ04CpvWJtlkVfmG0xWvLpXX5V3pZg5s/1/X9cnrTeNjZZa9WQxtZBTlUX3uE1ZeFx5pXkZNtAJSN0Mb/dlMVUU+kQl5pCLcYVVJiu2lO5uZSFZxLpw3WYRYswhohZnlyiKou5RFO0WRdFuABYH0Df9HkVRd1f1hkI8OyyXBZZbQRwVx0TKDgBno/bVr+qiV3ore0l2wkN1EqiW29Ka/hlsXXdMxa0sQozOYMw0PcKyYrD58NRZlOted2uWUQX35eU4yko9WXlDijlkQzkUddC7hjXX/pVXmGnGYxHlcoWWQyW3/Vfo76xsm+eicx/aXPjqIDOJDmERpFL366+b1eXyDabtmENZ5dCvvyZWR75w5VHKO/9nn5VPm2JqOfTMM3L1FJVjKo8PmuHN/iuv6O0QFxI2AlKbps+T7+esfs/qX7rKIdHeEMKwHiU/Y21SpbEEsOeqWyVc6u8WAnBf6996AAZmvi/ksN4w+LH2NVtZixQVhYbU9tMWxHblTvIIdqqLj6G6M1rNW4dc26m6lXXBzOKEktiwHNoNtTbzMSKrGnvbyiHdY0oUWA49qen2lkVX1hiRGzcXCeWQSswh3UtRU/dH9UHzAeA6/Fm5XGHMoZKVQ69hI2dlV0k5ZDOvTBm8QJcqb51NaYYJYxGyyiGgfhcyG3WkZPsBa5pjGv9KlW1LNJhNFV9jx5qVE8fVW8RlqbLsMn18jz2AJ590L0tKFcY3FzG7WMgoH6vQXnnKegGkE7g7JKoosyrOlENxHH8Vx3HE+fvKVb2hkL9vynKt4rntsOrXjR2iylQswJUhj5PFnOBOjn9tn7GaupWpWg6Nybl6zMls5e7MrSwWz4p9bmVfCh6CEE/Egtxj+fYeNsy8Ph3LIdF1r9vK3kD5VcRcqO/wKOqHN+JPtTJUpMu6vrdKj61l4XT+J9jAkFX+m2+2uw8R8pjE9BHlZblw6fDqq+ZlTJ3KP1Z0/o8+mmzprZK3O8NW/vvv63/LtuXw4Xast8qK7RQSOrI/+iggEWkhGPLnyNqA8oUXgI8+4ufJ8vHHwHPPtX+fMcNMPhN03ZtMlUMvvSSXrshy6KOP3LwQKSsmkO20F11UO/77ditj7SrIMWYH0JyWQ0FsZd+IzJrrp/cfj4ul0sWISrMcehGbmxciCXPRI1Dzx3P5x1SssDqgRVk5tC7eqvl+NSQixnGQXUx2mDsbECzAbcUc4pXjI/5UTf0lKIfy1+JQ3CCd99tv7ddv6lZWpxzSnIH5UA69gC1qZajIQ52ngHZZfmEez5ZDDz/MP8ZyJxs8uLjMqvSHqqBiOaTLbrvZKYdH0fC2447AAgsAU6bIl8lSDt17b/1v+fZ74QVgiy3q06nQCG5lZcQc2nFHtfQ6yMqk09Zbbln/W9p3VlyxOP9KKyWfqYwnnlh73IWrngu3MhM226x+4wRZy0NdtzIRrPHAdh1ZXLljnnACMGgQ+5iPcWWPPep/mzZNPn8zzBtIOeSIdBesFN9WFkzLIYkFAu8m6IGpyuckE3PoYwzENMyvVC6LyejV/uWnn7jpRIFaVc6vA1qEsU9YTEXPmu8/onfb/64sh2wG7y0zEPBszIMf0Ne8oBJuQxO3MheYWg7VxAEyeCpm+zcPHeXQLMhv71TlWBS+lUMq5HdzKeuNJytoctmEMHEsUwaXlkM//KCeJ4/q4kNmkSCyPGLBUg7JMH26eh5X175qyqEQCVUuFjbuvSJ0+nee775r/9/GbmVZxo6VjzkE1BqmjxtnXn/+XFQUGDbqs0l2zMxapfm4J3jXhjfGNaPlEMUMd4RvZVAen25lKtyAQ7EA5EdAnpVEnO3aMq9NJMrO15FPOw/E8WyKMAnYLdPfYkRWFTq2+7jonB/AblgYFmYrJY3qOjuU+VQOidgBj9f+wApILdGuWSseXvvoyDcGv5FO2wwPdRlcK4cez3WZspRDrF1kyuTXX/3Wn2Ijpo7LgNSyLLyweh5TXFzDbt3k0uUXJzruFzbdyqoat4fGebvuVvnybJFaK4nqKqo3+yLCdn/9xz+Azz+v/50lUxQBhx3W/p1nKWML3wGpeXH+eOy8c/v/hx4qX48LOnHMYnjnT8ohomFh7VZWlluZS6QWOZIrBukg1hzlkKmyxGTBJh1zqCCd663sfRO3BN6hLWA75lBtQv2YQzLoWA6pQJZDCWW7lblu9xDiqsSxPdcpU0za25X7YCht4wPdGBs6C0BbfdrH3K/KsjcaLtyk8qi4ZspiW85vvqn/jRdz6K236n83QaSUcLJ5ieUXAqHS0e00syEg5ZAzwn/lImM5FAsi9Jm4ldliHwxxXkeeJVAfGMa2csiXW5kt5dBWYOzdq1i+EwJ7kvXBj9bLfBrb1Hy3vZU9c8WiuIrh9QPXyqHALr83qhhzSMSNN7otv2qYKuM++ghYfXW5tCed1P6/6DpffbWRSEpYGKKYvP56+/8jRsjnywYFVuGXX4BevdR2pXIVkNqFpUK+TFtKZNPxphGeE41wDgBw553yacvoo4DcVvY2yF/DUF+yVA2yHCqGlENNAnMxwHIPycOJkGu6yLelJLgPf9DKZ9slykZ5ZVgO+XYrK2W7eqEATTCq58i2K0/5omY5pPC7IqQc4mNzzDJWEioSaruHKpcppm5lN8jH0K/BtiVWaFx0Ufv/N9/svr4vvkisK/7+d/k8Vd6trKWFXW+jxByySdlWHlVoU9sxh3iUZYEsupddnKtOeaZKMR+KF1XlUJ4qW6DLQsqhJuGfOKHm+wdYFTdP+H1hPpsL9jklxD83cQ3TLQuwbzl0Ew5WymvLrUyFSrqV+bZc8oBVy6Gnn7byisxmzCEVqjDB5eHbrezjjw3qK7ndhwwpTtPImE5eda/XqFFm9brApkyvvtr+/2WXJcFw4xi44AI38a7mnTf5/OUXufQvvVQbFNeU7FDv0zKibOXQiBH+ducCgLPOsluXydjNKs81Pp/TMnWXZTn05ZfAzz+3f8/K1tICnHOO3fp02v2OO+zKUEacM55y6MEH2b+XbcEVAqQcahLOxhl138fPXrAwn2gRobpYuQd7KqUvG2ZcJklsWw5dhmOMy2OVbzPmUGhB16WosnZAE6sxh/74R3c+GyDLobLQUQ4NG2ZQX8ntPttsb4BScTEZNo05FMLkN8R7Nb8b3j77AO+9l7jW7b23/frSQNayyqGvv67/rWqWQzbuBxv994knzMvQxcZOV81IWW5lvIDULjj9dHa948YBr71mt64QxlyflkOyLowhtJNrSDnkiHzfqeRCWgBrcfErugjzzMwcL2NnppR3MMh569u4viZWE6HFHLJRvgu+G+55OyMPZNv8ayxpXmDrCqTmWlbGrawa47Dr+6Ts+zAEZQOLEMzyXWCqHAvlPELn11+BWbOS/4u25c5aHfHILzDT7+PGAT/9pC6fCWX0AVbMIRv12hhvPvvMvIw8ZbpdmijzgXICUtvGZ8whVYYNkyunzG3fq3CNbcBTDvEgyyHCGb4XxbqoyH0MLhUe96UgWwfv4GocUSiLaJenInwr/3y4ldmOOfQ+VjeQRo5NztnSeR0hsyMeZf6uND5NmWxJmnqcK4emS76CDxCbz5Cyn0fNMukMhQ8+0M8bx2FcLxMZeMaNthePnTu3LxSKdiS7/Xb18rPy7rijen4g7IDUeWwtukxjbgHAMfaNt0tl3XV9S+AH22OXK+XQoEHAJZeo1d8MyqEyZDDdrSyEdnINKYcIISqLiI+wsvB4GQoUnrx52VjpQnIrU6UqlkPNgm9lYRZXigAX5zjXcVyyltisLX67yIeWJFGHlEP2sf0m3+fW41l8xRxqNrp0aW9r18qTN9/Uy2eiHHIdcygPz61M9Rya4c2+DlUJSK1bVxl9FGArH3Xqfv99tfSNqBzyUSdZDhXjPkJwkxLH+oqGqpBfHC6IidJ518Rw2+IAALbG08zfs+1/OY6UKktVUWK6WDZxKxuCfQvTxIisKnRIOVQNbAZfnx/T8DPmr8sTrT9YT7iSMd9lsRxcKxfLfh6ZWLK4ZIstfEsQJiFMfqugoOrcuV3OIsshHQ48sP3/BRcExoyxX0dI2HIrs2E5FCrjxwOLLlp+vVW4H8varWyPPep/y96rsqiOs42oHPKBqnIoTwjPR9eQ5RAhhLeIYP3eGbOEZWWVCZ9jWTPBOEzAQoVpLsLx1uu1oSgpY8EW8lb2jUrkWYlmUzmUKoaqirFyyNDySLoeCctG2+UT5oQyuTaVI4TzcCGD7TJV3MpM6SIO6WgdXwGpbRCqcshGm5alaK9izCEXsCyCsjuImVD2Lny+yw8F1bE63y7N0E6kHCqJqk7GbboZmbwNvwUHaOfN0xcTpNKpnPtX6I+XsammROr16WJzt7IqBqT2gWy/LzNIe7NirhyyJIgCT2Fr62VSnwgH08Cxtgkl5pALbr7ZbnldugBXXJH879qlRVf5VKWYQzzXTNVzsH2dQ6IZrBZCcn9z2e9lruW117b/X3RuMjGMRJxxRnEa24wdW/vdRyB8VZrhHiTlkCMadG5VQ37RW7QINlEOHYRbtPMCtYuh7mAHpTWJOfQ1+mvJlcV1MF6AYg6VQb4Nfccfsmk5ZJonxVebVMWtLMu2eKq1bnuzVBMXVoKPzsR2yBD7cpgSgnLIxSLP9g5UHToADzzQXr5LfCiHfMQcCqHvhYyv9qnCdSlrtzJb2LYcOu44fVkAYLibaB9KlBU3SgWyHCKsUZb7gWtEbmXfY+Ga33wvgmXhyen7bbrrBZtMzKGvFJRcpBzik+1jvu8Lskhqx1g55LHLk1tZ2DSSxU2jnIdrsgsZ125luoum997Ty+drK/tGxkab+rKqqcKYEKJiQcTT7BCpXKpwDUwp4xxN3coafZwCSDlUGlV9UytaRPwfavdmdWk5ZIqONUfpO/oE4Fb2FtazVhaLZlyUVkE51Ih93UW9cUmzM9ft04z3YRmEMnlv9phDZS0SszK6Vg7plj9lil6+MvqA7KIrhP4YCtQWbinTnXLaNLX0dO3tQG5lxVRTY1EBmvEedhFzaCY6O2lL21vZ26AMBaJNRQVZDlUDshxqZ67hBp0+J2dkORQ+jTB5nzsXmDnTtxTVYMaM9v9tLCJFZbhWPhVRlluZLRrhXmRR1sI0H9S7KlvZN+p1B2hc9kUzupXRVvaEEJVFhAvLoa6YiePxT+V8OvheMFVttzJSDslBlkP1+G4TXcqaFLDah5RD4XPxxb4lMOfss31LkFCFCfhDD7X/H6rlkC684NA2kXUrq0JfKIs0xpUOKu14zz36eX1hQzmU75Mhuar9+c++JXAPBaQOA7IcKomqTsZVtrJ35VZ2Mw7Sypel7IC7IWL7fMitTI5G3a2sGa+lz5hDNmnGa1cGoQSXrsJCroiqnUMjKoeyVM1yKKRFfYqNPv3f/+rnNWmTqtyPv/5qlt9HvyfaKaO9VeugmEOENfIBqa/B4Z4kKQ9XW9m7sDIIMeaQa7eyPXFPcSIFaCv7ahCi5VBVKWuCPBvz1NdNlkPBE8oCKrv9MVEOoe5WpsugQcCsWeXW2dJirx1DuRez2JDJZGEaYpvYxIVbGSmHyqUKlkONfh8BpBwqjRno7lsELXy7ldlCJp6Q7wWTa+XQFPSyWh65lVUD3/26kSgrIPVP6FFfN21lT0jyyCO+JTCnahPwUHcr0yUfc6YMaCv7YpqhfUxiDhFEEWQ5VAzNEB3RKOO3TbcyXcpSKo3DojXfG3EHJwpIXQ5PYdu2/33H13FlOdSUSqdGGdgJ6zTDoo3g02huZXnKWHjTPVRMoy9MX30VmDPHtxREI6M6lo0fX/u9GcYpUg45ohk6T56iRbCuMqEst7LRWKbmeyMqh2zWQTGH2HyMgVr5qqBEMknfCNBuZQRRDlXYyt52nbRbmb+6q0Kju5VtvLF+Xhf9hPoesc02td8bXUELkHKIKMCmW9kPWMhUHG18b1Mvwy8Vcz2kmENsuqB2v1HZc56Fzi7EkbYcUr02vpVZPmiJ/fTfcVgEM9HFWnnkVmafRt9GuWyqFjtkyhTzMr7+mn+sGRapthZdvXo17r1YNaVpmTT6+RF2UFW0//RT7fdmUA7RVvaEEJUFY5GyYHzObUuWZlmE3oO9nNdBbmXu6QQ9m+iTcIFlSRLIrcwinlYci2EcFsb44oSSNOW1c0yjLkYJOV55xbyMc8/lH/NtOeQC2a3sVQn1XrQhV6jn1qiQwqlcKCB1GDTg4yYM8ruVNRpVW1zQ4jfBqluZRlGN2KZ5QrNSk62f3MqA+TFNeNznnOB7LGKtrEa8dkRj0QwTcBV8L1Jd1O8q0GuofYd2Kwsf2sqeKIIshwh9GmRA4S0iZqJryZLYRcaC5n2s7l6QCtIDU3E9DkWHWDxC7o7/oDNq98JthkVp/hx9u/C4shy6C/voiKNdXxl0xy/MncJSQpVblQ3wum8RCIJQoBEthz77rPa7zR3SQlSE2FA0NMPCVBdS5FSfgw92XwdZDhVDyiFHNErnsRlzKCQaZZGnio1r9BN64G+4DHvjbmG6+7G7cV2NgO++JlN/CzqUKqfvNuFRdH+EKjcRBo3y3A8Biq1Si2/lUNXatFHvxUY9Lxu4DgpPuOe779zXYTqWNoOCtgHfRRCNhg2FBi3qEmy1w/dYBB1g8TVfA1FFt7KylUNVpSWmRybBZtQo3xIQIqq+6POtHCoDW4qPOAb+9z87ZYUGuZW5Jd9GzXDfNRumzwJSDhFNj8qC0dXisqyt7Ak1uuJX5TzNoICoonJoLjqWIEk7vtuEIGxz8MG0+LIJtWUtzbBIbfRrTgGp3dK/v/0yq65UJuzTq5dvCdzTBI8bPzRKQOpGcSujxWjC91jYWlmfYVlrZTUycQViDpFbmRxVlZsgqobtRXDVF9WkHKo+VQhIXeVr8OOPviUgqsCUKWb5Dz3UihhB0wSPG8KEEBZDLpROIZyXD0TBdlX5D/awVlYjEVrfIsshecZjUeHxUOUmwoDeModL1V0BfPct3/WrUGUFh0tk2uWee9zL4YqbbrJfZpX6PSHHQw/5liB8SDlEWCNkyyGCKAtdBUIfTLQsSYKscqjZFB86oxWNcARRDrYX+FVXDvm2HCpD4cKrgxbodpC5hp9/7l4OgiDChnYrc0SjLCJUFozX4AgnMthQOt2O/7NeJqFOMyggQjtHGXlOwEUlSNJOCG30H/xBOU8IchMEoQ4ph8LHZkBqQo9mb7tzz/UtAUH4pwkeN4QJjboYatTzCh1qdz6u2ibENg9BphtwiHKeRoklRxChY3sr+6oven1bz/iuX4VQr7VvuXzXX0Wq1O8JwhakHHIELSLsQVY+RJUIQfGRchmODkqelBBlkqGqchNE1dh2W7vlkeVQ+JDywi3UvuqQcohoRprgcUOY0IiLoUY8p6pAbV8ux+Ay3yIEi05fpP5LiKDFlz3efttueaQcCp9GdyvzLVej71bmAlIOEc1IEzxuCBO+w2K+RbBuOfQ9FsYr2NhqmYQczbC41j3HH9HHsiQJIbZ5CDK9iM2V88SzZjuQhCAI11RdOdQMi9RGVw5VAWq7WprhviOIPBSQ2hGNMr5Ox3y+RbCuHBqNZTAay1gtkyBSQlB8ZAlNHiBMmWSIaeZMEJWk6sqhZrAcskWow7RvuXzXT1SXKKL+00zQ48YRdBMRRHMSmuIjNHkAYE5F30uE2JYEQdTCettfdeVQx45+6y9DOcWbN8+Z477uMvC9LiC3MnXIciiB2qG5IOUQETwUkLpxoMV1+YTY5hfiRN8iaBFiWxLhQAurcJl/ft8SmOF7cVZG/bz755hj7JTjm1DlIogiqO82F6QccgQtIgiiOQnt3g9NnipDbUkQ1WTxxX1LYIZvt7Ky6rexCA11IetbLt/1EwRRDUg5RAQPWQ41DjPR1bcIzglNgRCaPFWG2pIQ8f33viUgGhXfyiGflkO+ymk0yK1MHd/3XSj4tlwkyoW6PUEQRANDCg17UFsSRDWp+qLX9+KsLOWQ7/N0SdX7IEEQzQEphxxBDwF7kOUQUSVCUyCEJg9BEIRLGlHB4NuCoayA1ORW5o6qB2Un/NGIYyrBh5RDBEEQFglNGROaPFWG2pIgCB/4Vg5VaXHoWwnDI1S5slRBxjKpUr8nCFuQcsgRcUwjCkEQ/pmOeQvT9MOYEiSpPi30yCSISjJ5sm8JzPC9SK2SW1moCo5Q5SL4+L7vQoHaobmgma4r6EayBrmVEVUiNOuSfXBXYZq56FiCJARBEG7hLcBPP71cOWzje3FGbmXVh9zK1PF93xGED0g55Ah6OBEEURXIIkaO0BR/BEE0B+RWVn2qsC6ogoxE+VC/aC46+RaAIIogyyGiSlRRgUDKITmqeG0JoplYccXqu5CxaAblELmVuWXWLL/1VxFSiiZQOzQXpBwiCIKwSBUVCORWJkcVry1BNBOffupbAjf4Vg5Vya0sVMitq3o0cn9UgZRDzQW9LnYEBaS2B1kOEVWiigqEH9HHtwiVgCysCILwge/F2ciR7uughbh/zjsPmD3btxTh8MADviUgiPKhma4j6Blnj9FYxrcIBEEQBEEQXiDFCVEWpBAhiOaGlEMEQRAEIUEVrcIIgiCqQKO7lYXIwgvX/zZnTvlyEAQRDqQcIgiCsAi5HjUupBwiCIJwAymGyse3uyJRDaifNBe0iiEIgrAIKRAaF7q2BAH85je+JWg+mkFx0gznWAVIEVAuiy/uWwKCqIWUQ46ghxxBNCdkOdS4kHKIIIDOnX1L0Hw0y5yyWc4zFFiKoDffLF+OZqZbN98SFEMKw+aCVjEEYYl+GONbBCIASIHQuNC1JQhaKBBuIMVQ+bDu5UcfLV+OZobGUyI0nCmHoigaGEXR81EU/RJF0XdRFJ0dRVFHV/WFBi0imo8t8LxvEYgAIMuhxoXGdYKgxYwPmkFx0gznWAVaWnxL0Fx0oCkjERidXBQaRVEvAM8B+BjAjgAGALgEiTLqVBd1EoRvOoCeqAQpEBoZurYEQcohHzSL4qRZzjNkSDlULlUYT6sgI2EPJ8ohAH8G0A3ALnEcTwPwbBRFPQCcGUXRP1t/a2joAdd8RKCLTgA/gLE3LNEQTMBCvkUgCO/QQqF8mmFO2QznGBqse5muQ7mQ5RARGq665LYAns4pge5BojDaxFGdBOEVshwiCIIgGh1azBAuIKVEGNB1KBdSthOh4eoRvwKAT7I/xHH8DYBfWo8RAp7H5r5FIDQg5RBBEATR6HRyZXNONDVxTIqJsiHLIf+QcogIDVfKoV4ApjB+n9x6rOGJY/27ndyTqgkphwiCIIhGh5RD5XPzzb4lIJoFijlULmSJSYRGEF0yiqJDoigaGkXR0AkTJvgWxzukZKgmpNQjCIIgGh1SDhEuIIuV8mFZrcyZU74czUwVlENk3dRcuOqSkwEswPi9V+uxGuI4viGO40FxHA/q27evI5HKxeQZR0qGakLXjSAIgmh0SDlEuIDcysJg1izfEjQXVVC8zJjhWwKiTFwphz5BLrZQFEVLAOiOXCyiRsXkAUdKhmpCFl9Eo9AR9OqQIAg2pBwiiMaApZiYPbt8OZqZKlgOEc2Fqy75JICtoyiaP/PbHgBmAHjZUZ0NAymHqgkph4hG4Rhc6lsEgiAChZRDhAvuu8+3BM0HKYf8UwXLIaK5cKUcug7ATAAPRlG0ZRRFhwA4E8Clue3tG5YY+nc7KRmqCV03olEgBTVBEDxIOUS44Pbbya0sBOgalAtZDhGh4eQRH8fx5CiKtgBwFYDHkOxcdhkSBRFRAC3MqgldN4IgCKLRIeUQQRCEHchyiAgNZ4/4OI4/BrC5q/IbGVIyVBOyHCIaBerLBEHwmDnTtwQEQdiAFBP+IcshIjSoSzqCAlI3H7SgJgiCIBqdDTbwLQHRqJBLU7mQcsg/pBwiQoO6ZICQcqiakHKIIAiCaHS6dvUtAUEQRGNACjoiNEg5FCCkHKomdN2IRoEUnQRB8KCYQwRBEHYg5RARGqQccgS5lTUftKAmGgUagwiC4DHPPL4lIBoVcisrFxnFxA47uJejmSG3MiI0qEs6wmQre1qYVRObyqHN8IK1sgiCIAjCFrSYIYjmIIrIssU1NJ4SoUFdMkBIOVRNbCqHzsMp1sqqEivhI98iEKAxiCAIPrSYIYjGoEjxE0VkzeUaUr4RoUGP+AChhVk1sXndqA8QPqH+RxAED1rMEK4gRUS5FN3LpAh2D42nRGjQbU8QlqCYQ+aYuGMSfFbDe75FIAiiQaAFI0E0BkXKOHIrc092PB00yJ8cBJFCj3hHmLz9ICVDNbF53chyg7CJat+k/kcQBA9aLBKu2HVX3xI0FzLKIcItWeUQBfsnQoCUQ46IYwpI3WyQUk+dERiIVfCBbzFq2Aiv+BbBOqpjCvXlahPR9Wt4RozwVzctGN1y/vm+JSCaBXIr80/2GtDYSoQA3faOiCJS8DQbFHNInYEYiQEY3fY9BLeyrfCMbxG80yz9r1HZuAEVnEQtyyzjr25awLilZ0/fEhBEAimH3ENtTIQGdUlHkOVQ80HWFnqE1t9Dk4cgCCKPTwUNKYfcQu1LlAXFHPIPtS8RGqQccoTJ8pIWp9WEYg6ZE4LlkC49MNW3CFyatT8RRAgcdZRvCeyiupg59lg3chAE4RZSXLiHLIeI0KAuGSC0kKsmdN2IUFHtm9SXqw1dv7BwscAiy6HGhbZzJ8qi6F6me909WeUQtTcRAqQccoTJw50m9tWELIeam0a6Zo10LiHSAXNrvi81z7eeJCGqCimHCIJwDVm1uIfGUyI06LYPEFqYVRO6bo2B7nWssktcHurLbsm3L21g0Ng02uS/0c6HIJqVL74QHyflkHuojYnQoC4ZILQwqyZkOaRH9lxDULCEIINtyK0sbBqxzxFuqZLlkG9l0rLL+q1fFXIrI8pCJiB1EWeeaUWUpsX3+EgQeUg55AiTyT4tzKoJ7VZmTpUXyXTfErLU9RWaHRKEM5Zc0rcEatBwQISCTF9ceWX3cjQyru73pZZyUy7R+JByKEBokVlNyHKIaBSasf/tj3+XVledW1lpNRM+aLSA1FVzgyBlC0Ho0aED3T+uqdp4SjQ+1CUdQQGp5VgHb/kWwRrNdN0amUa8juRWVsw+GFJaXfWWQ47LJ7zSaMohWiwSRLVYaSW9fDL3Oo0HZrhqP3JPJXQh5ZAjSDkkRyOdq81zaaR2KSK0c62ya5stQrsmZVCmW2hV23fQIN8SEETjsOiiviUgmgVdBQRZtbjH1Vb2LRTpgtCEbnvCK1VdJLGgmEPmkGLGDWQ5lLAGhnOP+b1/q9Hv7767vLrmm6+8ujbZpLy6GgGyFLDDd9/5loBoFlwqh2g8MMOVAi6OgeH8KQ9BcCHlEOGVRlqENtK5EI0F9c0EkfKxzDZy7VZGhEWjLZ6qdj5Vk5cgbKN7D0QR3T+ucWXhs8subsolGh9SDjmCLCCaD3IrIw7B9b5FsEIo/W9BTCitLnIrK8bXIqFTJ7fl0+JHDZX2OvJItbJXWEEtPeGHPn18S0CUQYcOdra7J/hMnuym3EsucVMu0fiQcihAqrpw0KGRznUcKICADtk+UGWlagSgB6b5FsMKodyXfTCptLp8Koe+mLGY0/KrzkIL+ZaAyKKyGOzRQ61sisNTjUCy3bv7loBQwcRyiHDLpMw0x2Z7d+xI14/Qg5RDBGGJNzDYWlmNtrhrBuL5eyDatd2O90yc4VGaWho15tB2eNxaWWQ51LzwJtBdu7otv6q4PJ9G29mtUZk717cEhAomMYeK8tL9ZcacOb4lIIhaSDnkiCq8+SHsQgs+c6psOYQOHYClB7R93RZPehSmOVgPbyqlDyXmUFXxtQjwVe+uu9opp9EWT1VTDtF8zD60oK0WZDkULtTGRGiQcihAaJFSTSjmUHOTD9wY0jVsVMshm9Y+ZVoO5ev6TdfvrZbfEvijfeON1dL7mjzb2kWm0Sb/jXY+hDq0TXZzIHOv03hgRtWU7UTjE/YMssI08puqfvOMt1ZWVRahMjTSuZRJaO0Wmjw+CKUNiizJbMrp061sg54jrJY/Fx3rflsc31qtw4S77lJL73OCu88+/uom7EALJPuQW1m1yN4D660nny/EreyXWqrc+lxD4xMRGqQcIghLhLKgrjIhuJWZyECWQ/IsBHNrGVU5RddWpBzaCK8o1aOKbcUUSzlUZVxPnht5cm7L+imLanttu618WhfyhsiCC/qWwIyDDvItAaFCzdxE4f6ViTlE2IPamgiBJnkMl08Ii1weh+Nqo/w6C8dl8JlRnTZYCl84LT8kZUCzsgjG+RahjSr3hzJk/xNuNC7DplJFdM73Yzdr9bDq6mC5veegfu93G9fU1sRVtZxmnzDvsktxGh4hKIc231ze0qRZrvU339gpZ+pUO+WocsIJfuol9NBVDoXoVtZInhn9+rktv1nG07I47TTfEpQDKYcCxPXCzHRBpSNfJ/iPXui6XZfHp9bKqrJiwQRTpWqZrkEsGuVBXEb/s6KsKMmtzHZ71CmHIrvlk+VQWHWblmGSP5Tdv2SVVCEos8rA1nn6srQKsU0JPi6VQ2XTSMqh1Vf3LQGhQoj3gwtIOeSBgy28Mc8zEHZjVtimI9ivDW0turbEs1bKMeE8nOJbhEoSWiBvW/KEpODzLcsQ7F33m4xMtmMO6bqVuaZDJF/3fRJWTCzLoZAIfYJ1SutQHuoiZKON5NP6Vrb4shLr399OOaET+r1EhEfVLYcaibvuCvc5Y4uePX1LYI9Gv1YppBxyhKgDrY/Xrde3IV6TTutjochTDtliMXzntHwZOmO2bxGaHhvunLZiDoXE51hGKb3tMWIB1Ps+2LhWVd2tLI9KSyyDzwvTNJrl0DzzuC0/f98ut5zb8k1Ze235tB0ddAWX45wtZdbmm7f/3ywT+jIJ9VlHsNG9XiHGAGuk+3n++WvPx/Z9FcJ9StZR1SPA274xiGP+Hen7Lb4pIbqVyWzdXPV2J8Jjl134fSqk/jYeiyqlD0l2EWVZnTl39VVwK5ORhaUcqmrMoQMOAK680k69vvA54Q/FrazssnXdaEJAZfFbtXMj/GASkJrFxRfrlWeDRlIOAY13PkT1IeVQgFRlYaaCa8uhRqMR+4AMptYkZbfb2WfXyksTdXlsuJXZDPzv13KIYg7xuPxyYOGF3daRv29DVyyoLCZCfPMvomry6hJiv1Kh6vI3MzaUy9tsY0cWHUiZQviiWfpekzyGw2exDvK7LG2Mlx1KUozOnMC1csjmIrEnJlsriygmuzAOYZc/lYW6aJLVrAo+mxT1h1uxv7XyyrxergNSN0LMoX/8I/ns1s3fQrQRJoIulC0uFTjNohwS4dsyLOR6CT1MAlKz0pehQG+WPubyOdMsbUjYhR7DjrjiwPeU0u/R5RHptC9jUzVhcpgugiKNhUwIu5XJohK/yRWkWNAjpJhDdA3d8x0Ws1aWz+ulYrWk61ZmgzInmn/9azJp7tRJvd6LLjKrO1TLoXXXTT5tuIWYUIWYQyFdN1UaQSkZKkstVZxmMXuPlWCw7VZGuKHK4xbRONBt7whlBUqvXu15HS9STMsXxVPiUSXLIVrUE7LQg9wtRffiTHQJQg7j8kuwHAppXPO1g5UsthfntuTXkcv34s7XtQ5VwdcoVLVNZe6hRlTO6d4P662nVrZNeOU24vVJaeRzawSa5fqQcigU+i/pWwJpdO6NRlEOLYtR1uohqo8wVonjxfi+uAO/xTNO67CFK3fBX9HVmhx+LYeaZMahieuFaOjlm+RvdMuhR+SNroPCR3B3IuHbb31LUD66/eT00+2Wp0KVlUMq7UNuZe7Zd9/63wYMKF+OqkDKIWeEuQjxBc+tzFZblKUc6oxZ1uohEkKLOVQVlsOohu+PRf1hfvxUkiR2yZ+X7ZhDLEJ67ri2JvGpjGFhOvk3yd/IyqGll+YHK6dFkVuq2r5RBCy+eHGaRkPXcqijhIdy1S07XdCvn16+Ru97vmC5ii6zTPlyVAVSDgVCmfeOsVuZhrQhWA7JnrfPLa2bnaoph1Qshw7GjSVIZAfb/Vy3vKL+MAT7aJWrivut7O3ulObqPmJN8gYPLl5suai3DGwtQnxOjn1sZW8S84ksatSggNT2qYLywQQb144CUosJxXIoBBr9/BoRUg4FSCMqIKoUkFpEWdtdN2IfaHTqFEW5a/gXXGW1PpeKNNtls8qz0ccXw3fGcqQ0kltZmUrWFVcEDjxQLU/oMYds123LkimUmENF57PKKu7K5qXv29demURzY3sxu+yydsvTwbYyh9zKxKjISG5l7qlCnwkJUg41CCoLG5OFwzL4TCsfz3JoGNbSliVL9pw6YyY7keQsWdSWZSmHiGqQffDGsQfz6opZWmWxIXtV78cemFbz3bZbWdn9Qsb1IM9xx8mnDd0NrQiTien117fn15Hzuuv06/aBrjJLZMVZNVT6C1kOETKYKId8bWXPo9EW+o12Po1Ms1wrUg6FguHgWtZi4CHsbNWtbBY6m4oEIK8c4sRh6VS/gw8L0YKTLHqILCpuZbb7TpUVQ7aw2aa6ZfXrNlE5T348LEPJZaOteIsAyaG1phwV16OyA0a7ijnUp4963kMO0a93//2B3/1OPz+PIgWOjwDaVVNWiOStwrlUQcZQCKGtdJU5UQS0MB5PZDnkhhD6Ssice65ePla7NnI/MoWUQ4FQptLBtK5uEccyR0CZbmVdOJZDFHMoTGwGpN4ZD5mKo00jWQ2V0c9l6ig6x7LuR9f1LDffWOm0IY5Bgwe7Lb/qE2ZfbmWuJr+hBKSOIr4sVe8zsjTLeRJ8lliiOI1JP3mIMa1q9JhDMm1qi0Z3KwtBBkINUg45Q1/B4Hryb7qo7NohUb4sgnHSeco8J9EOTi9jY6N6KOZQ2JyFM0qtz+dW9jGiprceUm3jUNorK/cELIiB835jtfwyzzOKgM03B374QS1PloMPBiZPlk9fJqp1//nPySdrdxRdTNzKfGDbhUWHKr8VroLsVemLOlSh/bP8619q6SnmkJhJk8yC6tvERcy4sqna/SSikc5FRAN0u0BR7EDGbxY9KBNUdiDjyediK/vl8Sk3XTfMKCxLpADyFeNkvopu2V02ZV8foWtArm+HophoJGxeb92xqMc8xWOKiAUxCfN3Misjj4++JgoIXESnTkDPnvzjVVqILrBA8mnSHqHTtau7sm25lTXLJJ6wT5XGG0A95ltVdivzRe/eenH0dBGNVSusUJ4cVYDG9XIg5VCDUNZiQLce3sLLltzZch7Artx0MovJEN3KrsCRXuotm6orUPKTpKtwhB9BmoQQLOwG9frCuIyV5/8a6+ENC9IkuNodztc241WyPtFRbnzKf58BIDy3soUXBrbe2k3ZMu234YZu6i4TX/eSLRpNIeCLIUPMy1C9z1VjDpmWoYvPmEO+lBD5c37hBbvl+SAEGQg1SDkUICEseGRQkbNM5VBv8P0TTOOc+Lo282K6l3plWRajtPOGEFRYu74Cy6GV8VHb9zK2h68yEUNxW2bMId9K4T1wr7WyGq1vqCpcTJVJJpPZop19WCy3nH59vth5Z/4xk4WVzLXeYov630SWQ7Q4sU8jt6ltxYCorfr1s1uXDFWxHGqWgNSi81l44fLkaGTIXVkNUg65olOJNolQW7y4UMgUUdUtp/P4ijlUFYWhKVVf1OYfQOvhTaf1Vb29ssSsx1G37sI8VY055BpXlkO2UFXGVCnmUKrcCCHIqK+JrEm9jax0cIFpe+29N7DyynZkqRLXXcc/5uu+EbnW2oTXZzbf3LyMRsFHH1h2WeCaa8qvlyDykHLIFZ+PVkre4ONsqTGHRFTVrSx0fLTLUfiXdzmKLIe6YBZWwQcA3FgONbyyo3s34WFVZW3Dt1crjTZOVWkhItoyl3Xs0EPV6wj97aUP5VAjxRwqU/YbbgC23FI9X5XuSRY6951rnnxSL5+t/nL++fJpfcYcqoJbmUr+NO3DDwPLL29Wb56q36eEH0g55IqhQ7mHXEzcVRY9tuq3UU7ZVky/QfGuQM2ygLRJKG0mE3DcJjK7lblaqFdpK3sZSxamW1mBGFV1K6trj4rM4HRcpmTKKbrOc+ao12GDOLanrGgkis7NxKWrWQJSi9pFRfZG7mfNhstradvNstFjDpmi0j5V242yinTpYqecKvQ9G5ByKEBC3so+mzcktzIZWSLE6I3J2BQvihP+7veWpLJHo1kCAMACmAJA/9xY+TpjdumKqnHjxMdDv3b74fa2/y/e7/3SLJLydTDdygrItu2bWNdYprKo6xNxbLWfmFy/3XCfNTls8csvaumrFnNIlpAWEbaUG3lklEMhtYMLqrAAadS2LxsfFji2A1KXHXPIJWkstTLvwUYYz776yrcEfDbcEJhnHt9SVAtSDjUIoS9AQ1Z45YkEd0VZ7dwMMYdY10zlOoZgrRRF/ElSGdfMRhtk5YyiavWzEIKZW+mHCrNCm8rUPP0wRqtsVVQnwd1bQ0+l28S7xrZyQxR0tixrgTIp061MxrqgyouuUNG9h4lafPRNshzi41Mx66JNQxj7bLZpqGU1GqQcCgTfi7LPMUA5j43dysKEP5raWAj+7nfGRQSHjyDnIVDmW78u+LXmu622Yt2bZdyvUnUUJCkz0P03WMJd4ZY7j07fGL7o9hiONYVpfL0hHjAAePll4Npry6/fhrJip53UytDhzDPd1yGLa+VQNk0jvHXPU6ZbWVntNlotDGfDccYZ7N9tKDFtbWWv2+9s9yHRTohANRb1OjGHdDnoILP8NhD1AV1X4RB47DHfEvihwpeMsMkAfOG0/CpZDrlm++19SxAGpn0iNIVjPj5JPuZQvo8OwOdK5W+OF8wElCC0u6joCtdYPUn0B91xIkKMJfBtoQzaBDDbXaPLx+iLicrnYyPmkAwbbwx07equfFv5VfPKXPrUJD4tm1XHeuup1WuCSswhF7DKb9aYQ75Q7eeLLOJGjqqw+OLFaVhtuoTEOwlbyiHdMmwrh1ZcUVxuFQJS66DbjquualcO21RZObTWWrXfqzA226DCl6xxaURFCu+cypRFOkCwrkiSKxedwSU0RUgeE/lcusm4Ju9WVnPMckDqfDlNsVtZAVV1K2v265Yn1AmX1litGXNI5Hp2xRXAMccAO+5oVodLzj23/X8TawiZ9DLlh9qnZKhCQGrXMTxWWcVt+SJc9B1emUXXz/d9zcOVXEcdVWwNWIZbmWkdOgGpXRBC/wlBBhZVfka4hpRDrhDcDexFSKB3jyVCCEgtvyBTvXatrLCCZPkyEtTWY9J+3THdVBwnGC+Q+y5kRxBLiBRFgJut7G1QG3PISpGlEYKCUIc6uaPIeUDqqrZVGdjs96Kt7HVZcEHgkkuATp3U8pU5+d10Uzv1yuR1oXBqVkJtmwsu8Fd3mcqhLCEEpF5nHbm0riyHjjvOXlk62Lr2tl4sVIWy3Mp0r0+V29YHTpRDURTtEUXRg1EUjYuiKI6iaH8X9TQSZU7cfSwSeHXakqWlJD2nlQDAOm4YBu3kY+cp3+WwWLf3KOtlsmJftB0rcCs7A2dZl0eHmr7laDW5Nt4pTHPpCtcrl5tVmjaamyIP13KKyrc1warSRM2mIsL0vEW3Z5lt6tutTFaOqlL2G+0Q36D7vLY77GC3PNfn4jvmkCt+73HjYB+7lTUqHToAm2zSGPFWG/1apbhaUe8GoD+A/zoqv6ERTc5fwibG5dvayt4GQS7IdE/R4dMyyHYyhHVOLpVDJy73kJNyi96gsZRDMSLshzvV6ikjSLSj5l8E3xem+Vv/h9v+33vz8ck/hR6gam1iEnPIlgym5bDOYQz6FaYpdqfVbJuAlBKq6VzkV31LqhO41LZb2Rpr6Odl4TogtauYQ7ffzv79uecoZk7Z+FQOLbec3fJklbq+LYdU7pnsOGdb7lSOFVYAhg/nH3dJlWIOha6wiCLgpZeARx+1UxbhHlfKoT3iOF4TwN8clR88y8+jGOC5wTs8zy3KlruZzKJPfhGn6VbmkNAth3RI5dI+Nx9RGHPMncsv3nbMoTy2Yg6F7FamEpBahtXwvr4wHEK4v6qmPK67V/w3IZOquAasv75ZfhuLC5EVZRm4DEgdRfZ3RQspIHWo9x9hDxu7omUpayt71r1Qha3s1xRv/OmkThYy12nttd3WY7OvkFtZOThRDsVxXN7+woGyabe38B5W8y1GMLh2K1OhqE7tuDENPvqsgJG+RTAiiuz3taxyKKkj8z8jgHRV8K1sSK9V0URAVc7dcL+1snzRCG5leWxPjn3GpPHxGHj8cTexjkT4VG7oWhfZyDN0qHq5LuRQYexY/jHeAtw3ru4jmXJV6rZZXlmB5l0rh7p0cVNumZjeEwccAIwYAWyxhXxd+XP/5hszGWR57jm35Vd5t7JmhS6ZQ1ZUWEzLh0oO4CnesSMAoBcmS2dxrRyyufAOoIXr2iWE6740+NZwKn0hxThGjANljypz5hRPZngxh1QpQ9lkS1ZjOSRniPNgdnseh/eI8/vPcEYcwvigQigLgCKKgszz8uSRWWj07VuchreI6N7dv3sfz3LItVxlWTCk9Ojhvj7byprFFrNbXhlUZYxonQ4L6dNHf7cyGVwaUcsEpM5johwCis+nDMuh7t3Nyxo4UK3OfJsusYRafl3mm88sfxFVuZdlCFGR7gJSDrlCsPtMoeWK4LiNRZtxzKEllwQAXIW/4Kij5PLxzqlMt7IiWYxxqB4Pwa2MV87lOBL/xgFO67BTtv3rM2dO7XeW5ZBLhYttt7Kq0VtDKclDtx1CUFLmMYk5pNoOjTTxy1OmW9l/DSI0ml6DFsu23i5iDu2/P7t8GXevRu6jhH2K+suf/tT+v8y0736+wapSvSFRRuB9G7GRZNloo9qyd94Z2GMP83J9B/l2UebKKwOXXy5fD1kOVQ+pSxZF0QJRFK1Q9KcrRBRFh0RRNDSKoqETJkzQLSY4qrzoEhHNMw8AoAem4ZhjJPMEoBySts4SjHJlXdMQLYd4HIkr0Rs/aufXjzmkXWVBsfJP07xbGYuQrx1QK1/a9UsJfu2hXZwo6GI7ZZq0h5W2LPGVWGiLIJsxkFTzps1uat0RUpu66ErbbCM+7jLmkKgeF5T5djqkfpPFp1xF7b/WWu3/y1gOhRDMXDbwtY2g/6HtzibD8cfXfu/QATj2WPn8Awbo1+3bGkX1enXoABx5ZHG6FVZoT28LXXfhUMe5UJG9ZLsDGCnxp0UcxzfEcTwojuNBfWVsqytCaIvCnpiMjfCKF7l4SiDXbmWH42psgyel0prU0wyE1p9VcSH/MssIzK4tu4Hl4+U0c19kUaX+2QPTAACn7jyitDp57bMCRmJHPJzM4BZYQFxGBJx3ngPZcl05+2Y+i+zE0GcMIdFE2NUbdZ77m2x77babmhwqbmWu4bWprEuHSR2u8L1YJOSxufB1uVtVNs1JJ+mXk9IMlnq2Y09dfbW9+nwi2+dtB/E3Yd99gcUXNy+nWcZmqUscx/FNcRxHRX+uhW1kynBNOBC34BVsYnVRmb/pe3EsSExiDi24oLJYbVyNv+BS1Jo3FbtXaFYmOQLykh07+HV+ngAWvmUoI5y6B8peH8ly+/QBunUTxBKwfM0Wxbia77Z2K8vS1kQ9e1ktV10OuYDUVussYbv6lI5ITM52XOtbK+WZKCKPxSV4GDsnX955p/BcTz4ZOPpoVQlrKbLyuOEGdj7b7k8uCGEirMqiiwK9LN7yru9bmUVHt27Avfe6lUOWp5/2LUF5rLaanXJ83UeqccZUF8ouUC3bxjVyGTDch1tZWper3bBuugk4/HD2MdPzMc1v4wUIq4z0ee3brSyKkvhN3+amW642LWgEyBPQFYKYQ4VZA1AE6KLqJlaGW5n6AtrtrEQrjkUAMYcIPUK/n2vka5uV+ZGlikRRjHXwlmkhdoQpCVkXBdu4Ug7ZXISwykoDfu61l3p5snWaXAcb52nLckjmPFIDc5HFkyvrMZ1ydV0NmxmfyiGVNDJuZbJlhfgY0AlI7VoZYPP+6Nkz+SxrpzgWe+6ZfNpU0LtEtl3S53WI/VqW+eev/V6FF1Q2cHILR1E0MIqi3QDs1PrToCiKdouiaBMX9YVKaItCF/LUTRAVLYTS33fAY0ZyyClAzM+/ijGHQlcOZc8thMDiqu3F3aXHsluZq93KWDGHZJp232iIlfpFqBikGu9+p5k/jiM8ht+p5bFt8VXCTnY20Z0sysT4soHtmEPzzgtMmQJcckn9MZWFjuiNuolbmQzDh9d+Fy0AbdW7xhrAdtvV/967NzB5MnDOOe2/hezm4rt+ESHLZsppp6nnkWmPbP82VYSU3f4PPcQ/1shuZTwl3n33AdOmtX/3oZg9//zk+VDg1R0MqtZyvmMO6TJlSv1ObqQcMuMPAO4DcEfr9yNav5/lqL4g4Y2PvpRGi2C89TJllUNFMYfmx09W5RLJVNT+iy/OP+5y0RXiAzVLaMpO38iaIbvarcyFW5kKXRdQ26+2DyYqpA7rZijq+/Ngtl652bfGkveX7n04AKOLE3Xrhn7gu7r5HqNkJ2a2YvvolMVLv8AC5lYGqnXaLGPeeWvTlqEc6tyZvc1yFCVv/LMysOTv3Tv5lI01IdpuvMxFSQgxm3xjQy6WJcbSSxfX68utTPecU+sXGVr3kzHGpcVTmbFq5pknsQ4xtRwqSltkaWWiGCpboSXrVuZCOaSDbvuwrgkphwyI4/hMTlyiTV3U1wiYDoJFC4Zt1p6Io/EvAOqL1IfaDMCK86oGnk7Tmy5y5fLL1XHeWZqvpw1jDpUZ80QHURt72XlKtUrJLpY9l0cVLUJY5dhQDn2K5ZztYFdjOaRS5vaMV/oC3sPq0mnbJ4d2+1VoFjW2JnUy120I9sGDaWwhnjD9+uGcPT+u+XlLPGsqXh26u4PZmJixLFF8BLDWsWpQrVe2f8WxhJI7V35+ws+1oLTUtqzt60VEEbDFFsDddwMXXmhHhrRc15Bbmbt2/s9/zOu16VamWneWzp2BIUOArbZyV4dOOa7rsBkrRvY8/v1vcfk653zHHcVpQiQ91402EqdzoeTzrcwm5RBhhrKTeSarxqKvaNGzzxbfoxP0lB474RHuMVnLIW7+1vQtgq4otRODVMwhOdm6dA1LCeKz3pApI4j7mhhemKZI2Wfj2i2Hz4zLkCE9FxmZ467dlcruh7HojJmCAuvrDCUgdSPU3xNTsTMeLkzXZdcdsB7eaPu+FZ4RprcxWZO9zjYmZvPNB3TPdV1dZRUL2bxLLKFetupuZS5xYTm08ML1v5m8kY+iJJ5H165y9Yviq5TRJ3wQsmymsM6tKK6LjeC8NhHJc+CBwN57u72GOjGHXN8rNucF+fp4ZW++uV75IlkXXVSvTN9MbDUC33prcToXyiFbcQB1yyLlEOGV7pjurGyXi5CzcTp2xoPKshhbDi09QKIuSXRHsijCKackuxKYEmLMIR0ZzseJSulDsOzIyiClJJF8o+0i5pCN9mKdoyuljLA9+/WrlyOAGFQuaRtqDGdPVmMOCWQp462zCBtuZay+zevvNieiKvLYrsOGDLqWQ6qoTuRtLkCE47fFc5JdVJVB2S5zslRFaXXMMcVpADnLFdY5i2IE2SaUmENFcrhQDrHCBJi4leVlrEp/luHzz+XShbJbmU1CHCtd0ECXrOLkBo4zLIdnylpZuNzKflGMw4PYtT5dgVuZyHJIhrjfb4oTyZ62YBQvartzzwV22kmyHkfsggcAAIviO6vl6vSbLfFcYZrSgnxbfjqz3t6zAlK7ijnkAq8TmM6d6+Ww3DWcuEZGFvpws8w4DJENSC3qx6pv/kKzJGBhw3KoKH/WfcZVzCGTBZVN6y9R2TbKvfpq8XEaDsza+cgjy6n3T39qj2vlitVX5x/zqVguY65QpsWJiYWi6Ljqi4oqUaRIK2O3ss02K05js53Jcogolajm/1jbPYt73NLdmV9c2XIrEy7aJEQ/6CCJytI3jEUyMipcGOOxLt6UqKR+60NZbA2gG+FVAPz4T7ZwEYtEhUhxxJdNrh2DB4n/P688Vh/fFk9Il82S5UDcIi9cAMi2bWiTJhfBxG1i23LoYFgwfxRXoYWNiVkcl6dIsI3ovjCJOSSTNq8MEimHdK/TUUep51GNEVMFRAoB2/hwSbRBkfWVLirtwbtnZK2JWHW7wvYzVSSrqYJcx3JIV0lnywLqsMPYecuYy4Q2X0opIyB12WMXKYcIM0Sm+RILTtVFKW8xsBfuUiqHx+p416yAHdiBfWUsh2QGvj/+kX9MeaHEuHbjsSjeHPRX8XVpzZcxgFCr1kJA6hgRVsZHSnl0eQQ7FratrX5smxNOcFPumDHt/8vEHDoJ5xvVtx/uxAoYaVQGS2FTRjwnOeTlqKJbWRuSM5zl8YljQRIOwi04DhdJpS1zcmZLOVREGfFldOrQdaH6rsCIVGdxYdut7LHHgJNPrv/dVXu+9ZZ8Wtsxh4rK6t8feOcd/fpUqJpb2cMPJ/LmF+MpW2xRXIZOvSrpL7kEuO662t9CaWNbliwulUPZOmTcgOMYOPZY/TpEZfPSZXn9deCvfxWXw8ofgvWVy35Z9a3sQ6y/LEg5FAiuBgnXb6hlLYd4i01bbmUySC94DdzKjMjsR2oSc8iVO5Or3bJKQ/Imc9VerHJ1rmujUNcejKfuMr1/LEka/+0rFd+K0zelZJfVWufuk5oYXI5cEmTLlVUOLbQQ/xhrcmczYGUZMYdMMAipV/O/aJemMmMOyZRjujDzoUCxteV42VjbgdHAzcdF/1Oti6XAKKovX3cIfUDHXcq1a63LmEOsY67wbVXUqZO9eny5lZXtxk2WQ4QZhneDsnvWuuuKj2ff6hksfnXzxrE4X1DxWCyMZGPH8t/y1yvUWkeblVbii6SxcE0Vb7baNl9O1m3NZGGdzeuyH9TUo+FiVndM9o22QEkX7bUXAGAwXpcrTFA+iz32UMvvW0GS56DVh+NFbFp6vactd2/Nd9sWcm35ynwpULTnsoutRSSRvR9lYw6tsQb/WEuLumJABV8uOrKT1rFjgUsvrf0tBMshFi7b0oaSzGXduhbIjLj+xrLwKLJIqyrZNnn6afFxWXTuiaLdrFhy7LyzXJ7zzYyWuW1w663Fj5o8555bW67PWEo6lkMy6dPvY8bo7VTpgscec1t+GQGpya3MDaQc8sAsiJ/6ooUGN45M717Mn10rXWQth4rGepdypm3WNUq20daJOdR2SHIRuNhiwPL4lHmsztwUdhdlNrdQl6mnTJbt+k17/R3dD1+q/ZIVkLobZgBg37tRJ/lZFM8SSSTjBhtIF1+Lo0urqoiKEGNTvKxctg7Z/Kv0+NqoLJewzlPq3APaMsR1zKEiqwKXrl9lxA0RvaVlpc+y6KLAiivW/64qt2xA6rIm76qxQ1RiNJW9AIljfauRtdayK4sIlvLClXWhLKaKye7da7/37StObxqfSKcsUZmbbiqXt08ftbry8GRdaSV15VDXrmxrT5vxsIrubVaMIJO6efX16wcsu6x+uSp1AeJz6NHDXb1Au2VSly5m9YQEuZURzhgBvoWIiJPwD2yA/zGP8Sxz0oVjjemqw0U9VznEkS9NL3Irk31b/PLLwM1/+6ju9xXwCU7HWXiw31FyBekicMcQwbpGdUXrWA4tXvDayYCDcBM6Yzbz2IWbygdZVuXJFTKRHrfdxmrZPTAVgHxbywYcvAUH4iT8oy1QOAtXylHWAm4P3FPzvUZhoyCG1ts9UdvWataK02ugek86R3H2qSL/33FB7Y+yM/ZWmZhKKM8GnmXFHNJJmyKrg7MdsNS0HNX8LnYrY+Gqz6noSn30e13lkO97FACeew7417/MyijLQivL+usDr+Ye06FZpJjiWnlX5HIqU262rVwvxl2+KAD0FTe67LKLfP0sslZcKrDO5Y9/TGJBnXOOXpl57mKE0HWhwBNBlkOEM2Te/LLS/AOnoMNvt9SstP0O0ln4uFIoycTFmTNHrqyNNwYO3Hosow7gLJyJJbuM1xGxBmHbmboSWn5QRF271v3WE5OtlP13XMg99pe15HZ1Y1HUNwd0bb++cff51AovaOC/4Ko6GWy4yy2K8cm9q2vxwUkrcx+zFkD3YC9uuc597GXHnlQkw5nhenjDKL9zMqYgtlwzU9bIbyKgqrVwiGvLIREqMYeKjtlIbwtR2+jGeBKVV4ZbWZaixbVq39B1LSxDaWFiORSCcmiLLcSbhLBYfHF79ev2vxNPBFZYofY318ohF3XpYuu+NTFSLdvq0ObYr1qPi3FykUXM8m++uR05gMQ19uKLgQUWsFPeXnvV/ya6RjvtZKfeLKQcIsxwFXOIM+qWtbuQbuwNXr61MAwAcDT+xS1zNttApY3lF/tJnKBNiHLtAfnBuYvTWQlInb6ZzrT9mh3ely5Hm0w7W9+ZL9N4Jn2euUC0EDfmmmsUy4uKXcPWhb6yTXeSZlMZfGJmRzZpyyFL6Crf8vdoKFvZm25RbzWdXnKjMmStSEW0tCS7CenUL0NIbmU88q5HOreeyDrAZJtxG641MmW6jDulw1JL1X7X3vW0IgqGXr1qv9tSYJa5ZXscy9fHS7fccuoy6dSTx6XFjInlEFBuzCFZtzKbMcps36MihZrv8UAGE4UUxRxyAymHXGHwhJLZLl1ajNRlqaT4MKoxhxbERMSIsBWe5ZYpshzaCK/gkyueUZDQDBftaF2B0gpLMbDg4GUweLByUVbkkUFl8RtZfiroB1tv/3/ffdXyyrTj8a1bivMUhiK5ZZRDrDhAHWBhFQ7gVvwfzgdjf+pioRIUxlFWW2orU1StRTwHpLYSc4ijeXDhYlenfJNsPlsxh/70J3H9ZSi8TOqQfQvNa9cFFwSmTlWTJ39cZDnUvz9w0kni8orKV0V2d6gUn25lrPK++KL2e5Uth4BiOdZbTy9/Ge/4XNy7PLmzVhVlWsrYtGTJK1GKlENbChwgbFvpyeKqLpVx2Qa6z1ZW/tNOE6e1ZeV13HHyaW09q1UtG1NIOUQ4g7lrkYQ1iYhBA6YIj/ueMPBiDq2NdwrzFr4tVjy5DfGaUnpdVBdVtmIO5S2H8kd99wVd+mGMWQE1VkeCZJK7pxVOIBWumc4CXNetTIaopx07YJexe2y5SKaIrtc8nPhaxiha2cmmE+1Wllf8KY0tnseOlVc2L0NlcmcSc6jswJUu3apY2I5vznP3k12oqypTfLqVFWHiVlYVbLg2uq6DV4bOoris8YBXj22LIhFFyiHZ4PllIFKm2Lzvy7AgclVmVXHRFhSQmjCj5Dv0wN+yF862Fmeyb+VVlVoH4ebkn7ydvy4S7X4sNOty9CpYrMzRp22RkrnNXfWHMrZD1w3kziKUAV7G+ieftojsrh+q5t1pH+zQd0Hm8SXQvlucaUDquvNhvmJjr3jHoB++Qn91AaRpvx5fYUl0xUyHdUHp5s/3lQgtcvGoWgeEaeiBF7AZDmwde+vSBjy73H574FP2JpDS2Iw5xAqQ6XJTONE9Z7LgUnGPAcCMOSRKa4Mi+bp0Ab76Kol3I4OK5YTOOayzjvzOUbbqFOU75RR9WWzUr5qPV45MvCbT57sLZaCO+yMvj+haFtVTxvBeNDaIXvhGkZ/5mY/HnovztOlWVoYC1xSTOiggNR9SDnmgaJEXIVZeYEcdxHdI5PlK886mTeqFF9Yqtw8mKY8OPrZgF6ETc6h3b4lyWc1iabQWtqHivpWyljo98FON/J3nce/aZmJVIeUuGNXXWZdGsb9mt0lVdStLn5a8fItinJIsItmlFJXdujN/XgTjsQCmSdelVCdQczGXzCjErKPpJixVdL49Wi/qvPgFm+ElqeeQVD0lT6pNY3SoKIeKzo0li4uYOTJ1lL2gym/5bc1F0mBxs+SS7QGFfS+SRf3UpdULL58o4LPNviOKiyXCRawUV2XYdCvToV8/e2VlsbHluIxbmagt4rjd1W4+hT1HbN8v+WOq5XfrpldnmRTJwdjTRrssETL3Bi/QdpELuW1IOUSY4erul3DEfhzbualbAtWt7E25GQcpjwDayqGy6pFAuD2myBKpjKfSb37jruyM/AP6c14/WTxHlcV4nS+0QiwpVj0LdZ4iLE/K2khmEcIIxNwBtU/A9E2lap82tlTbbjsjq0LtgNRK56m+05hJu8gok9kZVRVjDMvQSK0oFnfcIajSEj168I/Z2sr+6quBNdes/125mS2hsluZjfrXXjtpg6qiG3OoLLcyXSSmiEGg246s88i+EDFBRnG89NLi9CplF9WlwkMPAR9+KF9Hvq4BA4B11y2W6bDD2BaTWXTcyrKcfHLy2D/gAHE6HlttBay4olxaWWstFf71r/qg/Nl6bN+LurF/iuT47W/N8tvi9tuBNzgbz8oo91wovxsdUg4FgrTVgcSdvx2ebPuftQgxXbDJxmtxzaJdf0Rvy3FHilDZyt7mAtBWQGpbFmSyigqX/YHbD3MHfvc7xXJLtJgQlbF+709q00rKlX14ySyAWNeuQ2YnuF13Ndt9KIvsbmVtyuROnYBjjrFTuQJq965+H7fSh2Trlx2f0nT57ZOyeQ1ua1bQdtsTLtHWuTJv/mSuy+GHs9Ol91zZ8Q5M3mjGsZq8UZT8HX440LNn+2+2camICS0gtes6y17UlGU5NHhwrTLC9XmyFCgyuFYO7bRTEpNN1zXyiCPk6l96afaW4tl6iu6t/FiVV2507Zo89nV3PVtxRWC33eTS5pVlNmIOHXWUG8WEDLZcwdZZJxwLp/32SzY5YFG2jGQ5RHjFRDmUpX23MjvEiNoHu+13UMhXzKmnsn8fMqQgo6hNrryyPrnuYs6WSxZHEWhL6SYqz5YFl0ierIujy4DEXHIzirIezjpuZSk67cSzOjJ5y902UeLInrcoUkXXZU8G0/JKdTdttd+vmYjq7njGcENm9ideZ+jZC1hjDeCGG2p/j/lWbaEjWpyY7N6iUneZO9Lw6ivczMGwTtHvtilqz7w7k023sihSv54msW9YyjrTGE9Ca2ML1/DII83LyCIrUxwD115b/1sZcti00kjZfnuzMnUDUqu0dxEhBKRWDcDtahxjvSxwOWaqjmsiVOPQ2eRvf2P/bssNW4cVVgDOOstOWaFDyiEPFAVytrmVvVPOOafuJ/5W9sVyn3MOsOjC9U+NvffmZGBVlW+fv/ylsF4VbFwbX8oK/o/m2LKSsrIQ5cyiZc16bWxpL0vUqmzRCUjNtdbK/KwbHDdrOWSCqy3epa2oNK+l2m2iUccKkjbvOWwqatrasEMHYPjwdhtyRqcRWeKE9EiSwfX4q3rP2ZKHteCSVQ7ZCOLLKlMHVQsSF7FGXOQzrUcUw0SUL6Uozosp++0nrj+PLQsHIAn8/cIL9usoKkN03+R/l7Uceuwxedlk6mXV4RrVmEM2FCcu3LZsyKVr/aRC9plTtecxj0svlUtX5vmOHJm8R2sGSDnkitDuUBN5csGiVSxcfodHAchbrGiJmc20zDL8dH/4g0bhGRytKsq0HMJ881o5DVklmfVYLDIdxMZ+1xaQizmUwHb/rC1Hx91Nyn2BoZi2pRyaDf5+zBviNUFOv47dSrGmDOppuz6GzwupOETKqzZ+v9OdiC+5pHxaV6gEpJY5ty23lCvLBqpuZTYth3jYfvOuY6FjG5aCSuf8dK2tFlusOM0227B/5ykn11kn+ezcWVyuLnmLrbLcynj165DmXW89ubp0yi7Cl/JRFhnl0y67qJWR/e5CsSOKQZdvB934PTxYyiHbY5aunKLwoDKuxhtuKD5uo/1Ycf1k6xCNCXEM7LyzvlyNDimHPKASqLb+gOabcJMlzDffIFp1lbavKgPbg9gF08HeccgJSy0FTJ3KPnbaaQDM2sKFi4XObmUihK4/3eeVLkeFYHaAmzoVWIm95b3stdONOaTlVqZwP5dhOZTm7RDV9qH0nu+IuXW/iZiEPtxjm+ElTAV71laG+6NJ2hrigPp/hqIt6tuOczUj7elEE2hZZswARo1Sz2cblhLFZGH65JO1313GHEqRdSubM0deFlGaNK6QTFpdbJVpw62M1R/KjB0is3nrgw8C06fX/847r3XXTR6PO+1kJBoX233CRR+TteJYf32+HCKrFxGuYw7JwlPm2nQbPeII/jQc0Ivborqhcba9f/iBn861W1na58p6HsjW88037B0MVV78bLGFer0q/PIL8Oab4jQm9f71r/p5Gx1SDgWK7ZhDRb8J6dy5bcarmrcT5qI7ZhQvnVrPy8oAw3tNoOtjk2Jp9JNRInTBzMI0PNIFvG5gQhlEiorS3nx1YFTUo4f5dVZknlbjGK0tthVWHLIum1ZMoTPKoezCSFUJ0h2/1MqWy98DP7V/YbSFivw2FTRK9ZbogsdzSTaJORTFuZl6qyYgWqB+HFWxusnTtas7qwUVVON0FJ1fp06132VjDtkYJxddtP1/1oIrVQ4V1V8kq4xLU/58UtlkF3VFFie2n2eqyiHT+mTrlk3TqRPQXfG9m8iComxMLYVE2HBpTMeqvFxLLFH7fcEF3cokQlZJrFsWC1kll6iv5S0aZe6vom3VReNZa4g/JvPm3pfqBKQWHWMpJF0qUtP/RU4UADD//HZlkKVfP/m03bq1z695mIz/oTn4hAQphwKh5uaWcNn5/MIHcPqJswrLbQtIbfFK60yU5N3K3L+eM1lEuogH1XaNMtkXwgStsoB2yyGbk5HrcGjNd9U2HDAAOG8LRlAAjbIK4cUcKqqndZagqgDt2RN44on6WAGmW9nn08hak9mwHMpLo6Mcuht74jBcW5xQA90+k23nM3EG3kC7/0BNmUr3cngzjBhR2/msusJMvIV1hGlrWGcd4IEH6v2lGgSZt9bp4qVTJ/VhXTXGhGicHjZMnHboUOD555P/Wec1e7YdOXSeJUccAdxzj/521IC/CXwIiwYXSi/Ac8xDwfEOHYCnn5bPzyvPxC2I1TZZ5e9//gOce27t8R12AO69V6/sPDbkda2ULho/ZcrXicfUh2+ELEQ0/kRRMjdN/5eVJY8oTxkxh7JEEfDss8BrIq99j7z9tt3yWP0tb+nKolm2pNeFlEOuMByBiyyHBiz0E9Zbp/w99SLEeoOnQg3KhDCTs4DwzYSG5RAPlevXr9M4AMCiGCcus+C6zT8/sNPyIwvrsxJzSMM+emmMRsTbK1OCbbcFevdWz9c2IWGdd958nWUdssIK3DLz/6uQjzmkoxzaE/eiU0FfLMLlA3wZfI718JZxOToiau9ilFH65H/nsVz/2VgH79R1BuG13GUX4Su7Kg+5Mm2fWtzkrYJkMN1VKks+3kLeumaxxYDNN689lkVkOcQqV4TqsNqxI7DHHvYMOWXjksj2TTXrwPL7vKpyRTafa0zq32or/XJcnXd2DNh991rrx9SyUCacJS1G29HdrUxkAcRDNK37/e/Fea1YYZfgVpYPSL3llupueKbInl/W2tVVvXvsUZyGEEPKoaoS5eJAZJ+qhvzrX8kbPxb6MXdkLYc0iy+Zw3CNtbLarEdEE16NmENOt1JWtKCSkWVNDCtOJNNBNFcjcUG+445TE8UothgY7mKstAWuWMqWQ4PWAvbZBx0Oqn3dllZjcyt721gPfi7gz7gWx+AS7fwpLlw8ueQ6g+z524o55Is77qh926+iHNJ561tm3JAsO+0E7LNPsuD517+S31wFpHZxjqwy//1v+/WIaBvncuNm1rWWBS84NA8bbmU2Sc9tueWS+0UHE8sLk/pcIzsGvP66+HhZMYdsB76WGftV5c4rh0RKGNNg1b7G45RUueh0Pu7gnbqOvK+/Dhx9NLDaauJ0Tz6p79Zm27KLYEPKoQCRXXjXdHiOSr3NZUmh/qOOEmtedQYi2XszaLeyzInvhbuFx23Lo2M5xDP/dbEgrYk5lIkFxNoJi8ehuN6O8kBCI1Jn1oyorZPy5LzoIlPBBPJouJVx0yveqzXldu4M3HknOvStteFmWQ5pTdRE51DjD9D6YWlitzRGJ1VItHO2fh7X4nCshvfbkhq7RhqcaHHd4mAuprKHrtDfd1/gL39p/y7Tb7NuZaqoBqS2NXHt2hW4807gkUeAQYOS32QthwDz62irH6TlnH8+P26GjuuKShks5ZAo35lnqtfviqLHn0iuI45I7hcdVGMI6faXonYVbS+vg6ycgweLj/uQSSWv7ftXRH5uamUH3Uy9On2gyC1RdF4ybmVlKYd8juODBwOXXQbsuKO4rG22AaZNSyzuXaJ6Lptu6kSMSkLKoUCo87/mvaHXVUAYDhh/PiCJb7QUvqwZ5HhB5xZZBNig4xtt3+V3H6pN5ytoWkpHzMHOeDD54ngVZKv4vHLoRJyvXZbWrlHr8OObCOtSVAxx42jpzn7i4vg/Kkgp/URuZQXlyexWJrWIYgQTz5spy05sVse7cgmLhfKKa33HEUckn7/p07rdUFwfVJrFUbhcqvzsNS1cTAUYM8klMjGHsm5lquOybEBqHWTLTJVaKruV2ajXZh2qW6LrLgZVj/lIr1KGjuFsvqyVVwaWXdaOPGXBql/V4lHWKkbrBamG5ZBNKwlb97CNclTKsKn4kK2Hha57bKocUrHiHDw4WUPJkpX9d7+Tz1eE7nXSyZ+y+eZAr17q+Vyy/fZhbKZRJqQccoVgpJFxD+l4w3XF5UqMlLYm/gf9cTZiRFgQk2p+79IlET3VuKb1jRsHvNZ963Y5JAeJfLrLLhOmlivUgDmYBw9i1+SLoyeTbcuhrFtZHAPn42Rt2WTlqfnOmVVyAyiXFJC6CNty2ApIXVSei93KeJPUrNI6n2ZFfIx3kQuSksOl4i372764o7AuUR9WkVOn1xxySNKmveZrjRgseaGOllQOAZnzSQXM1VHUP0WTPd8LQRNkgriaWA6VEYC0qP3zyqGiMlTftNtaMKvAcy8pw8WtSv3dhrvIhx8Co0ap1ZsvV7XNbLtDmWK7nlBcW0JQFKuU4Xr3NVF92TJEOwOK6tJRDr3+erKGkiVb/wYbyOcLkeefB3780bcUtfz3v8DMmcXpGglSDgUKd1KaGQUWWVh+1KyZCBoGfZaeGDLcRIrKz1uDSA3wik8BG25lNtGJOSSTVjfoX01Zkm5+On1KNbBuq0ASBetdp1ixcVy452UpkoaXVznOUNb1j2E5BPhzK7OFcl8TXNx+GGNDpLr6dMcl3rnVnZ9ih011vEvjCytvtEMhlJhDLhe5MsohW9ewTHcUFjylhM1dm4oU7qzfBg4EVl89+b9vX/m6dGSS+V2Ej/vZRcwTFhttJF+nzfqXXFK9PFmlZFo2Yz8KbSVbmW6wOnNTFQtmE7cyHiLLERm3Mlfx3wC9e76M+8/FMy4kC7hGhpRDrhDcFcxFSVb5grj4jWUUYY01NOTK7PF3Os5q//2uuzQKq4e34Mq7Jz1/9GMYgYHZBK35A+6SrpzoJbKrLBxllE2miOThTlrj2hmBDSuSG/Cn+h/zgXclLAVaBQJgz4JIpRxWW7THC4uZ5dlyKwOAXqh9VcNTDokwbjeLHTbaZefaohUts3jnMgID8R5Wz+Xzf+7i85OzOMtzwAHAq9gQf8B/hOmqZFUByLmOmFgOuWwP2W6i49rm+PHG5NVX6+NOONT/S5ehunBmHT/xROCf/wTeeCNx01IpzwSTsl3n/fRT8/JUYw7997/Aiy/K5VWRo4jttquXS5ZHH2X/PmoUMHZsErPl1VeT+KB50noOPhhYe+3646p92YZrXh6RosSGtbMty0JR3m+/BRZaqLiMVDmkEv9NFZvjia7luYkMpKgJj4BX4s1F1Kn2UnB3EVa8A+sWACut1PbvQvih/fcBA8QFGb6Oyt/7feb7FQNRvLW5C6y7MRmSyiNaiHSC/JNFtFuZtbe8koqKtuMSTS6lLOrWreZrf3xVmCal+NzlXbxsofTw1QhILUOMCKvig5q8POWQaLeykGLXRPPNl3yKlJiSgf+zDMRI9EFYNs/z4edCN7vkB44ykefuGQEb4n8BXVU7qMQc6thR/X5SDUitg2x8DNG58hZTLISWqpGcTCw23LDY0qdMpVXbOGdhZtyxYzKPW28987IAebctE9ldB01ebjn98rhzYkF9UZTExUwtuFSQ6Ve23aPSOtPdnvJ5llkGWGyx5P8NNxRf6759gRVX5B93FUNF5jxZdc87r3m5NvLJKpYWXxxYaqnidKFaDslQVpurULWXUVWFlEOhkAviyzVnV1TrtlkfsB6cDpQk0nFR6jKy5QxmILj8cuez1L324h9bESOxCV4qruq//0V0zTUA7LiVmSJjjSHdD087rWZv4yiKsAWexyk4tz5dK0880f5ztham4kyxcYoe9qaWQ7LliWIOAcBVOALv5qxduHUUKIdCUayyWkvkOsaK7WRLmeVTKXb++cBr2BDzYTr++bdxeBzb1cuUNgtn0FCKr5T2A8unXOY4L2NJmA1IrYqq1Y6LMVlGOWSC7vXac0/9Om3HpNG1HJJ2q/eEDcVWGai2WV4JKcrvep5TZde99Lfddkus2/bem5825fTT2//PnscTTwAPPlibtmjM6dcPOO+8+t9feAG44AKgT5/6Y3nZbcG6r0XX6ZZbgBNO4Ft2sXA9FgNqYxnv9yefBO6/355MriAro3KoyGOksWAusDrVvhaRiTmkVGeU/8cNsgsN7k5T+XQyxZUxM/vzn63VwytG9CYnQoxT80oQFttvj2ir3wKwNIjy3uZKWg5Jbx0ukR5nn123hUMHxDgXp9Wma7UaARKXBZu+9FHGcqYoQJ2tgNRCtzIJoY/ANVi9ddt1Xvn5snV2KwtFcaSLqvzt6Q3OW2NMycp57LHACkh8NY4/YCKWR73fRsxRDoV0vcpczKnuVqaKbJwilwvMvILKthUEC5k6RBtZyi5ibGHTZSI9PnCgOJ1O3bw8Re114IHmdYSEyXPcdl6bu4iJ6nI5RnTqlLxcyESa4JJ1i8yWv+22wM47q9V/wQXsXY6XWQb4+9/5+WTnIXkZTCwb83kOOAC48EK1HcFklE6m2FCUbrMNsOuudmQocxyRUYyRQkkdUg65wuDuyMccOgg31ViNyLANnsTeewPH4hIAwLoDf0oOZO+Snr0ylcpbxZx5ZvK5zDLy2etMoh3frUfjMqyJYcxjWouiKDIe8Q7B9Rg4UML9o5VsIMU8ouazupVyjQFClPlZ4K7Da6acQKI4OzJsusFsqXTSLm0xWwmTpWOH9mOzZklVn3DOOcyf+y8wGRFacE5ewQXg2KUfBgCsi7cK5cqSV+ywuBjHMn9P06+6au3vLLcy67fwn/+cKdvsXpO5VcVuZcX5rVgMZVbvOuNS3WKiTdlY/Ghvk1/jQrreLcolZ51V/1v+HHbbLfncZx/18mXbY/PNk8/DD7dfh+rb6hAnz6lM6bCw5ZZ6+XVgLdJ9t5GtALtlnYfpuJDPXzSvcTkOrb02O76PClm5L76YnSZ/DiZKKKsvxbLzQAMXVJcUnW+6purcGVh0UffypJTxfHRRRxwDyy+vn1eVAQOAM84oTpdauvHYeuvqzUlChJRDgZDvzNm3jzfhT3gJm9VnENwBT2I7DBkCbIqXESPCQr0ZMWtYtp0S7LhjcvPPP798HtmFlFIATcGxy3AMhmGQfGFSFcor0Fj8AydjxId85VD+3F95pfa7tFVWa7LswuB6HCIjojYyLmNFi1/Vxfbii6rZ6WZLF/UzkRwdMru3SVsOnXpq8seg+zyz0YKO2O0fa9Ud23TBj5J7FxNqy6uppF5WmWt8LC7FJTimXtZWevYE7r67/bu2W5nKDb3uumpl52DtuqabX4pD25VZoVjhMGMPReLVgpSFWwNNtnbZpf63fDddbrnkt5VWUj932ZhDiy+e1DF4sHzZqpZDKq4MKm+fXfaHfNnrrpvUv8QS7co6mRhIqvWIjsm6ldl2fdPBpOwyF/ZFihAbfc5WO7/9NvCvf5mVmz2fY9nvZgrLV7EAVH2fKVtnUR9x5T5l2jdT5dAbbwBXX11fJusetnmfSm+MooGOW5lM+h497CrZRcc//7zd8EDESivVypSX76mn5OTwrewPHVIOBQrXnH2DDZJPg8h+ojhEVskGpNZ80AsnrBoiadP6pN0cLwAAFsN36kUgZi/mJd2PZJUnojdsygNi9kEpUPRIBaTOVc5yl9oRjygK2M4CCxQkyLR9XX9EXLebGouOUXua2XKGS9DbVrAeZj+J6/uFiWKEtwiyNokp6Otrz/dxXd026huMNwAAy2FU2zFxny0WoF1GA2FL0Lq0dWtOXbGsjy/a+wFNrPiUEfPFhuWQ6v1dtHj0rUDUtawRKXuy33nn99vfhmUd4Ps6pNiWQ8USxqYy09Z5qNxjNsZXG1ZIOnWF+mxI11S865kG+k4tOm3he2woy9IulHGHsAMphxzzySMKe3e2ItzK/vDDgdGjk9dpoTh26hTn8U279plEEU7H2fgS/TEAX2iXkYe5SJ04sf63Ll1ripk4EZj83QxuFTFHscN7WIzECpiMnuyDPDn/+19u+ny9MgqlO7CfsLwaMquwH34AvvlGPisTiVlNx0i+30aIk9dULFOFusQSyojVVi1MI1lUkq7Aqov1dsb0vi1SIr60ypH4DovCtur3UFyP0S9+g8F4s+7YJPTGj+hV+2NrI9qeKNZhOcKz8Jrm6mg7buhWVgXKfFRadevVxGSRmYn5bx1rSl8FC4oissq8NvfZ3MyY51Y2YoRacFoTdONNqZTjc0qp+r2oPFNsl5e2+4kn2i1XhiuvtFeWqVuZ7rhoej1YseCyZfbvD3z5ZRLa0ladWUxiYRURqmKmjGdgeu4335ysAwg7kHLIFa09duHesuYFtWRi6taXu/TSmkK5h7f46z1fbTvwBrP8AC416JWlmo8idEQL+uNru0WzFsKiLRuQDLp9+gA9F+5SdywNbJ2L3VxIb/yInpgqlXY2WgOot5rr1MQjyr5F6qAW0bULFAL5ZGbwffuygxzKEiNiPsg6onZLsiV6TGn7vyiQY4TY3j7GAKKFFqr5HoP9Olv6DSvjXuW9RZdRDi2M7+Uqrsm0cM3X7h1nYlGM14o5VLRb2dLLdGCm743J6IUpTMWKyBrNW2wFUbyvzLF5MR0A0Kcnw6UYejGTXJveu8LEmsfELN82NgNS83Yk1J1e2FTYqKZVlSObTiY9z6JyscWArl3VlXEu+0jZu5X1yunVda9bkVsZS2Eni6kbmA3S+UK6/TkLXp9MrVpkYI3N3brJpS+SSwYZtzIXz8689VK+jqzl0LzzJv/np9n9+8tvKKAql6+A1LpU7QXQ/PMn6wAWsuey4IL25Kk6pBxyjOyb9vzNvfbaEgkNn3g12Q1Hl6Ls5/5xVM13XvpUKdYHEwvLLd36SPHVlXSMIMnzkC1v4YWBW28FHnuMU1+mmGOPBTqjIHhOmi8j52IYJ5UHDz/cnr/AbUt5sWowC2Y/LOp/TOP9pDz3hxtxB/bFf/Z+GMPY8c6FPIBdMArLFgkiDyM/r5u++cj3ePHF+t9Zu5XxquGl+yNuw73YQ1gGk7feEh7WHZbqJqgHHpTsoZtB2N8UK862y7bb1h57+60YTz5ZXI/OOFD3dj1TxtZ4Gldd8DMuPfY7dmKZeh97DPjoo/a6KzZhTEkXA2VQ5ttSHjJuZV261OlmufiMOcTChnIyzcd6jKi6acjI8J//FKd56SXg9deL04nQcfMz6bPDhgH33MOXQxdVy6EU24prHZcdFiecAFx+OXDQQeoyvP46cNddcml1A1LrtC+LIuWQjWsydKjc/ZQl643x298CV11VG0eKRUj9R7eOsl5uyLjnlo1K/ddeC9x4oztZqobGRq2ECswbxiQAqK+7zVAR1b1r7ROjqA0Wx1hMgliN27ZI8u3UWwKqirD/+z+5dIcfDtx2yTRMRF89ZVvrNeYttKMlf8PNytuiXRrLTz2ZHfQWm28a9sUQYLVVAc4b9g6YixawXz/tgocK62ij6FUq5zfeaa77e/ZKUKRsyFfDu1YH4Wb0BcMVsogllxQe1p08pfGg2uSV2NdZeSt7TlutsgpqlEGrrxFhnnmUitam1voJOOKgX4GxrWOvzhi2ww7MrFVVEpWJz0eGbEDqffYBLr00uZ42FwVVQkY5xDtHlfsi613MK2+TTfj5ZSn7eiy1VPL3/vtq9dtetOYtR3RwaRHZuTNw5JHiNLw6l1yy8FFZWJat+9uXW1mWlVYC1qrfw6OmjrzcWYugKAKOOKK4HlsKM9k0uugooEIat0OYT2Q2yyVAlkPOYd2ARRYS0osUx9Y+Omhv79wqTJsJpo1tollcdhmwzTbcw8cdJ8iraDnE3cFL8LZfhEmbyMQcKgwqzTquMKqrBrEupAz7+V13Vc7yCHbE5ni+ziWNicJNqLNbma0JuK2YQ4Vcdhmw5pqI+vRWzpqVbQ7bk4qbXgtO39eehMvIc9VV/HTLLQestnr97wX3qMx9OGQIsOGGQO/eUkVWDlvnI3i0WEHVrczGW/wFF0yuvw1Y9f3jH8CqqwIbb2xWjowunVWGzBvvojE1HW+6dAFOOSWxEslTlrtX0TV1GfvEJUUyrr46MGgQ2xqkrPPbcUfgjjvMy1l00WQXw1tvVc/L24HL1hhXhvLHBiK3sjIRuZXZkqXsczr0UPHxMvvAmWcCK6+cWIPx5JB5XhC1kHLIFTllh3ds2/ga5q8rLrVAcW2+fvTRQtvBiy7iHIgic2UcZ8t6WZwpzEyI661/ShuIJWfbsru2xawfNV7l7oDH8Ty2tH61dF1UZcrVCUitc00Lz2HQIGDYMMQdzYxaZZRDQiQaUfb0rY5hotedXbog+m/OlzSKuDMkFeXYVlsBr77a/va1ajGHTFA5v6zFmM8YEzpBsXkT6VdeSa6/q+u82mqJ1cn888vnsRFUWcWtrKjMSZOSz759gXPPBS68kF1mUZw6G+gooVzHgLFB0Xl16wa8806yV4tNVNxlHn4Y2Hdf87o6dUpcybbYwrwsG+lVrLJ8udXyFLjZNrVRj2q6qrmVybDzzuXUI8NKKwEfflg7tpKlszmkHHJMB0hEZ4PmjaV5N8oqGbp0UZ/McBcciguTtkCyokGvjJhDyyyTqVCtvW3HHGJnVpRJ4D5QJAfzeGv0UplzzcccMrYksv0q1odtcIkxh2TgTbrK2q1MRh4ZZmvsA6B+bhZnQwon2jcXBysbrZRZDGfVb0vZ7HxHtyZCJx4JC1nLoRTVYUg3voRpQGoXiyAbj5HU5Wc/yc02s7LOM099UGcTitrh97/XzyuDraDcsgo6Vn2pBd+gQXKyNBK+F8K8Macs12qArczyZTmUl8OFDC4UUKJ8yy7LP1Ykj046XRr9xZVLSDnkitAsh4pgCDptGvC94gZE0gqRfDKN9irFkuaaa4Dp0+UshySF99EnZF3cRDDbe4klBNHTxfVkd5LSQrW9M+mZCw+fsyqJc5FVpun0r2ijDYXHS3Mrs0ReOSS8tCNHcg8JLRQ428a7vr/rdhT86ivg11+L6+YcjCRfYGRJ2+WCC4Dtt1fO3kZRQNCycHHNdMq84QZg4ED+cV23Mp6VjIwLqux5lDF8iurQVXCpupWxfl9uOeCnn4pj/bHK+flnYPx4cT4Viq7XAQckdbrG9J4qsiIXlb/rrsk5rrmmHVnKwqac2SmrSDEh634pm551/OefgT/8QZxPpbwiWO3o0nJIpozQdivTHe+ffbZ4R0vfCkrCHApI7Ritm7akmENFpFuiy9QlfGPNSFAkulPFj0q7degAdO+unk9FHAl3swh22kT4hkG3/O7di+MVxfx+vQTGAAAWwg9q9dq2HOrZE/i6II2re07maco533zbyyz48kSdOgrT55VD+S1gZXGpXFKNOdRG60BnS8GjlVdzNhVFqHklWzgOWyItMt1dUpeaZ4xHQpnMduggt+20rFtZ/i0+b5tmk/NX6e+2hk+dMY5Xhg23MkDuXmAtFG3fAzKy8nbvs3Ef6F4PVSVF0e+sc7SlaAiZIgsV0wDFOsoh27tFFlkVsv5Pxz5Za0pbqFwDm3XIHGORuvfyrIN411LXmtQXv+HvlUOALIecw7xhVBdIb75pRxigZlRycePadqXyPrgYzEKrYmVhOxCCbJOl7XPc67vifuyK3XB/zfG3sA6+2PCP/AIMYg7VsdDCiLfamnloNJbG21hbobBWPvyw9vvBB8vnzfLvfwPPPouos5xdtt5bpGLlHpBcs1eW3A8ffJDLX0JQdZWy88ohVYuaOCruW1bv7iiyMl6ouJW1Hda4JqrbH1cZV4tdk3yyRqxFyiHdCb3L667qnsbdYEFyEWtiOaSKTiwoVWxem1Gj7JWVx5ZbmY26UlSvS5mLfFl4/bpLl+Rz5kx2elm5itIXKV9Ug2PbsopJLYdUXhzZvJds9pW84kb40lex3mWWAR5/HLjlFnW5qsRZZ/mWIGxIOeQYK0oQ2xH2ysJgNJSKYYMSVigGjsK246tkW9PqwqNTonTQijmkAC9/p8FrY1c8WNda6+AdLDXPt4IC9ZV1de3QvTvXKX5pfIm1MVSpLgDJFgpZRI7aonPp3RvYckt2+zmMOcQLSL1R92HZMDdBYhyQWgqOskXnNnE1htkwsZAsnkgwbRMbbcpTQvAsh1hpfaO7u4+qIkDGcsgWZbqY2FhUF8UWUSlLFRebk7iwXAsNlnIojtsN4H/5hZ3P1ryBpxyyqWRXVSwCesohG7iwqMm7ctvuj9ttZ24VHAKidgnFajlUSDnkitZeqbOgDsWtTKcuaWXYrrskzu+bbcYsXxSQertt5takUcbUDj3HPSe+hxvwJ+1yjawuDK9LXfa6/YTjtvy2rT5qyvvHP4DnnqtPNGsWvwCD/p9OOBZcsKA4n6+OrrsusTZKo2tK5r/sMklxMu0fRfyyo0gi5tCfD2v7927siRvBtpKyZk13+eXCsm+7Tb1IXgwh1fRHH51LJ9OFLK0WmXWtsQZw2GHAPfew82hck9AUCSxeeCHZOcoFgwfzj5m2TbqIufJK/bJVlUMy70BCWBDrKoyyPPAAcNVV7YvYrHIoLX/LLc3qKMLl/VOGdZIIVr2PPFKcT9ZV0uS8bLmVuWpb2/0sq4wsUg7JortbWZn9UaQcmjvXrBxdXJ4/a/ewlLLGc5PyXLVNFeYpoULKIccwt4hmLLK9T7wsCVBzboK31lG3rondYt++rRnl7uLFMBY3XK3iM2IRTj17bDIef8JNhXIU7tDm2NS2KF+MCPjLX/jHTIURneBJJ7H3bRW95rFgyVUUQLSN/BbiOhdA9Um1+OLAjTe2x8TJVZmeT/68Fl442XpaFplYOyzlUM3p7L1327974l4cjJvlBdDhyCO5kZAHDEj+spQ5SejRo7y68jCvX8eOSWD97M6LWTTapgpuZZttBpxyink5rHNM9bWq+VTqS4Pp6pSdjzmUXi+RW5nuIiLUPsC753fZJRnORX24U6faMdTWAqsMyyHbofhswNohre55VmApxDsvG26YtvKY4rLONFZMXjkkE/hbxa3Ml+Ks6Hg69vmyHJJxhdUldRksg1DHe8IuAT5GGov8jfRHsF9pb80Od+JEEC0LkCLFB+uwYATUNjHNWjsEHtNH2opK9jz+sIeBNO2oPZjk2rvWCoVT0ja1nVzqvHX2JFegcLLu+jXYTjsln84HAD7JtbNrGeiTGisIgbzroTWWG8PKc62+3wAA9t9fUA/nd62xLXIYc6gAVzGHRG1H8BG16a67Jp95y5Y8eeUQy0oGkBt+bFjrqCAqWya2hqxsvDZh0bOnnSG/DOWQ7mYBtjBxa5Mpl/e9TEJeIPPugz+2hm8UWT2qlJ8l+95hlVXY+Wy4GZ52WvJ/0c5jWWvnFF+WQy76isr4sdBC9usvooyXcYcc4r6OZoaUQ47JDwy3YX/mImD11bkhT8JAZx9IAXUDpoJbmf3K3eSz6oYVx4h3/0P2q24xXNiL04yliMH5xDGktryvw6JyiPX2q9RJXr7xL7ggmanFcTIAqObn/aZB0YS+xnLIcSwx+cJq5ZC10kwZgC+4x34z/2TEMbDjjupiafUpzTa1smAyuAlEWf/97+qYdbuQ00WZG26YlMtbgKXkXXDSBVHeckhm+JkyRVyXqzHU4XAHQKwcyp7TU08BXbvaqdO1cijrPhSyAsOEsq2AZBSStrBxzbJzm2x/22yz5HOFFeyUn6VPH7QFml58cbPyRZx6alKHKHYaIHYrCyEgdZn35rzzAvvsY6+8MjwcZLj++mJZGnUMLANSDrkifRut8yAL8e287M5QYKzAgbqGaNsOcdVVk89+/ZjliCySnG53z6Jo5axZjOz1NgkCzHMdku2fhS5xnGthPDjbtBzSUQ6F/nSJIgzGG6LDxUVIuJWlC6kgxyYJmOdleG1rcg/guGz5RuIcy/CQJNQxaeO85RBPOSRDt27i4z76AqvPFrkp5UmVKBts0P5b//7J58CBcnWqYqIcEuXheYz6QPbZmj++3nri9LyYQ6E/on2h6u2fph80yI08qUJ7iSX08uu6cKb4CkjtIgZYiBZNrPKqME9ohMDbLiDlkGOKFlNvvw2MHt2atoyHXFy8EHTNu+9mTJ9PPBF4883aGRocWw6poKLulxS2bnIjueBeay3g6aelkmrhQtlm7GctCkhtgVItiPL2zKpKxZi9DcjNOAjvYnWTogv94osCUte037Bh7EquuQbRvN3VBBPBsTbUzV9zbtljn3wCnHCCuKjX/6dWtyPKGi9V7ptx44AvOEZaoUwedV2ZdMt0kS9L/l2OinIoW/+bbwKrrWZPLpUyVC02VPtS797JXCQbvP63v03O+aij6suz4V7nynLo7beBkSNrf1Opw6bLnA7vvlu8dXYZsZR0n5u28D7fBXDRRcA77/CPq7RRduw/7rjk3tp0UyPxuORfevLcylxaDo0eDYwfzy4jlGddSITQ37/4Ahg1yrcU4WHXV4hQRsfTRovMXdgNMwCU7MaWqb/GhL1jR2DddfWK1N3K3vasnTPqu7CySPuLyaCq9dYSgpgo886Ljih25NZ6OBa9tpYgfUPcKapXrkg/uG28rlRxdmfA28q+G37F6nhfubwumNn2KWqH7ESLF5C6Jh8vku4CCwAdNEwXLKDc97LXd/nlk5Vkjs6dkv7UteNsoFcvZlbWd/mD6pQ92ZKpb5FF3MvRSKRWtTpWPinpdeme08UusIB8GR068B/NOtangLnCQsZiSAWWO13RdMSkvgUWAL77zr5rVK9e7UOQrwWXyeJXxqvalWuYSd4QF/ys95kqcnbuXGs9ZNJ22bE/O57YtpzLwxqf0o0iXLmKAcDSS8unrSqhuJXZom/f9n2RiHZIOdRAPPII0Hmf3YGfGQczd/R5OAU9MA377HMqDjig9UdTFwtL7jltDzOOFYC0dcB//lO7T7kNFC2H8rJGL77IzlaCq04HJAvZFrbxiZQc3La/5RYscc21wFmMMjlehsLyUi68ENh9d0DwwJXhuusSN4Hfjv+kThbtbq/zpBc1vgw55ZKppdchuAHjsChOxj+wV3Rc3XGu5VAJsxydKlj9V+reSl2AZdLOM0+bq+Oua3+DU3EOjlvzGwCbqYjaTtaS01dA6kj9lbytLuBz8njLLcCBByb/hzRxv/124IYbtN+ZtHHFFe2Bq5dYIglxtuee5vK5xtRiyNabekte5DU88UQyNdGJyyJbr9bYafE+TMuyZGhtLZ9NXMngKuaQbHoZuUIaK3mw5L3yykR5s912euWYymKz3XxeA17dIdyXKaouxkQ95FbmCg93yu9/D2wzz/OF6ebHzzgXp4UdALsV4URRtDjefXdgM80FG08A02u6xhpWigHUB7vUsmfuXLunBABYaCFEZ55RmEx5gD7hBGCppfRkytCnD3DWWUCHiKE8kD1/Gw1lajk0aybjR7FcosOdMRvn4VTMi/a9bXnXiGU5ZL0fMXA6jOoU/re/tf3bqWOMc3A6Fug8w7hYW9TVLSOMjkKpTHdMR7S9GHGE7oR0kUWA0083b9u//hVYccXk/ygC/v53YMklzcpkUeU+IMLFgqJ//0IPVWu4suiyhWqdvNgtMuU0ah8V4cqg3tdC29QltVcv4OyzzSwydWQKte+5lCsUZUyobV8FSDnkGoW7JN+R//nPTKDBCy8ElltOX46S7hJuQGrVcjjuJ1YsFyTaYu+Fn8NuuE8u5lCJI6HuZcwqh3jEiICNN2bXK2HVcN11/G0zY8Zlq2pwYwBe3Mrq4i916lQXyD2PbNcsctfYaqvk8whcrVx2ljTW2Bk4Uyq9kfn5uuuKX9GrBJrZbbfkM90PWCCc0saOmXp07gcrbzY18jSCckiWZjjHLC4XPKZtadutTKfOMpTiMnKUVceFFwIDBsiX4Wo6pOSuq4mNuFKyXHZZfXBmVy8OQ7dasR3zytY9mublxbvSdZM96aSk7LXW0pdNBZt92HQam+X44xPZioLRm5I9/733Bnbd1W19jQAph1whuBtlFwHHHw989lnrlxNOAD791FyuKqj9AWDpAcxsWeWTdswhCYasfAHuwx9qf3RkJ12GkiRVDgk9m77/AVh4Ye06Dj0U+P57/nHvbxMy10dZFhvCG7qV1VkOHfwnoEsXozLz8OIILb44EI/8BGtjqFH5nTsnn3vhbmE6rZgc+fvozTcRLylveSa8DwcMSBpkpZXYeXOT0U8+YSarx/JNUbYiw7Q+72OCQ5pNqWQb3b5hq91D65u+5DnhBODzz4vT2bZUKUMZpFK/bY4+GvjmG/vlqrqV6ZYfMrbP22SaxZJl882T6SAjlKF2mWXBUw7pKOQ23jhphzJj/gwZAtx/f3n1VRVSDvmgv7mrjBEqUSkVKcUaROnVvEV4I17q+rTppnrFtraZzENX98EsYzmk7cInQRTZm1RoP6gHD1bPE9Arl2h28c5tuU3/pMVXMaIxuZCpv39v/ChMpxs0XQtdcwRBmuykL3TrAp8xh5qR5Zf3LYEeRX3L1vNLNeaQCmW9qfdJqr/WedyVia5bWR6frj22x0EXsZ+KKDqHvJLJhZWHSNY0qHVqvWxang7pSy2TehrlmSkzjQ3tXEOTpwpYVw5FUdQjiqKzoih6O4qiqVEUjY+i6KEoigx8ohqI8eMR77a7v/qjCBgzBpg0qf53C4gWaJPRE5M3/J1cObybeaGF2rZZi2NPtu15Bg4Evv462atTR5wSLYdsmoSqwHIr0+WHH4AfxboFNvvtVyMPUPIbmLzlkGqD5NzKarLfey/GjweefVavaKdss03bvxddlAw/fTGx7bfJk5M/Fun1mTwZmDKFczBl2HBufhlqkhp2DOk3YZY7YFn92db904zWNcOGARMm+JaCj6tYJWWTHwO//x545RX5/CGdl8rzbv31ga++ch9TK4/rZ44rl6sy6vWNrbhMq64K7LOPfXlE8i2xRGJhdfbZ5vWq0rr3BFc5pNKuobn26fbrOXPc12EL3/VXGRcmGL8B8CcANwM4BUB3ACcBeCuKolXjOB7joM5wyd/FCy9c2Or/396dx0lR3nkc/z7DDDiD3DAgjGIAD1QUEA/wCB4oXvEgarzFoGjWC9ToSkSURDGynmswiGJiYjSyGlc3ijFC1CjZoBKNijFZUSFENIhGQECo/aOmZ7p7+qiqrrPr8369+tUz3dVVv6566qmqXz/1PJW20igbT6dObV+vcC8q+PHs796xo7rqM6lfR0fzK3rxkd0XUdg7fql1tN12kYdQiqM+hwI4ucuOt9LBujIyQ5K6lhVMpu+bsvPyc6VUmpkrNPpeJr7OnSu5I7DN7HyRaaGYNcx7oW6SunYtP6uC0+QFa7oG1yKyID9W1tZb2889e0r6V8Wza1M/OKgwPLU2os8hzzp2bB2uPk6amuwkRKbDVletCctM72aaoD5frD+8YuLU0XFWFepIEB2P+63custvIexH3zHlRL1f+p0A87t8RlWu8vtmKiTIvsAqGRel2o6RmYRZvmr7nmkXxG1l70kaaFnWNZZl/cayrMclHSmpTtI5ASwvngrsKa+9VvSt4Pmx0GeflZYsKTlJ0cTW3ntL99wj/fjHjhaVf/Hx2wKDsIXeoXEQCbSQVNpyyI91HafWLNOmST/6kXTSSWUn9U/+yndbIO64o/h7lZbNIiPBFFyG5eA2yP/6r9YKrwKBlhm3V78eZ1XS2LF2T+4zZ7bsYwceaI88mQRxPCF86SV/5xfH7xiEp5+2+2NwkqwNg9t9PzN95keIYrchuVXol/+0lAk3/F4nAwZI999ffv5uykmpGK+9VjriiMrnE7VCiftS68hrp8p+8nt9+tWNQe/e0gMPSE88Ufm8ojz/zazfa66pfF5uWg5FLU7XHEnje3LIsqy1lmWtz3tttaT3JfX1e3lJMnRoCAsJshfHQw6R9tjD28eNkSZM8NzfUWZIXm8Lr3D6Sj8XzGxccdIhddBxxami3mor6YILCg+Pm5PgzF8plXyJCm8rMz0K9F5YZh5u+xwq1iG1ayec0PpzW1LOKKNYvjF2T+5ZP1n36yedfHKIIXn4UJz25Wx9+sS/r5W42mYbeySXqFV6OM/k4IPsmtCvxBNKO+us1nWdf6yupMVjIRMnem8R5xc/Ww657ZDax99KXIvr8USSTj+9fKtDJ+vOz+/odXu0jH5dwTySlBzKiPpUMYlCOcQZY3pJGiTpL2EsL27GjZN++cvy0wVSgOfNk44/PvCFF6z4PNSG5SrS3IvXEPb4AI5amVk2Nkpj9ZRq5P5+K7dh+d7n0MMPSye66zsrkBOA8eOle+/1fbaX6lbdcovPMw2yw6e8/dftui61J/lWL3mYUf5HHn1UOvbY0p9xXc7cxnXMMdKvfuX/SGNZrfMCH6npBz9oXZaH+3PjeluZ13jifHEStiDX4amnSsOHVz6fUtNnLl78Tg4FedsK2so/FwzjtjKnjjlGGjHCn5YYYSi1T02ZYnfUftxxhd83RjroILvx/403BhKe79wmxipVav1WW13hZLSyuAgiMZcWYf3+8R+SvpB0f0jLiw/L0rx5rq+jvcvfQ8eNs6+oEiJ/J858nVhUPD7/XHjkkdJTOtLXeRZTLDlUrulxy0hq+ReQJ53kLOOZJZAK+r77pHMqv1s158Rflm7VZE2aVPFsc1U6Wlm5fr184Oi2MndvOZy5M8cfb+dlPMfgVKmZ/fd/52aoyizYaVx+9DXneB18+9sePtQqrsmhOKm2E9JiyRE3ZaBXL7tD7iBlkkPt2pWezg+U/1ZBlff8uibsdV5oeV27Sn/8o7Sjz8PsBHF7VTmDBkmLF5fu12rrraU//EHabTf/YsvmVyPc7L/jVv/GrUNqr5y0HIrbuod7jq52jTFdjDE7l3sU+ewFkk6XNMGyrH8WmeY8Y8xiY8zij+M8jIcbHmq7iRPt5zoV6fHLD8X22gpr50zyaye9U9F8MvJPCAomLkzQP6/L+1mwpBNUOilXbFPsXHBPyuV2cxVLDoVViVuWfdtCtgmakzOSVawVy1q6UWGP3CXvcKu05V+B2QwbZj8fkz/AYIFC4ympVK4JUDknnOA8hkL8OhN3uOBMB6tO+hPy7c5XBzPyXI86mz3gil+3lVWaHCo1olI13FaW3UNAEi7mCt0CLjkrL35NEzS/byvLnF4FMfx8JXbayX7ODAjid/kLu5VflLfkhS1Jo5XBO6eHuBMlve3gkcMY8w1Jd0q60rKsx4rN3LKs2ZZljbAsa0Qvx2MAJ4SLWu+WW6Qv1UG1qqCFQeD3IxR2zjnS+vXSAL1X0Xw8NQMMsgYq1Avl3LnShg1lPzpe92u9tmrzen642beTbNwovfGGp0hLyu5zyE1+MNOawWuH1NnzzIwQJtnl/CZdKT35pKf5+q1Nn0P33ef8A075NVybizic7hrr19sT1te3vrbbbvY+HUin3Rs22J1WF+Fo9Y4fbwcYBJd1ipPJM8PhzpxZZB5hd7BfgSRcTLoRxCEkqSfGcYnbaxnzu8+hqPuhCcorr9jnG9nidOtWm/OkKljnYchODh11lH2I3GuvaGPKt/32dlznnhvM/Mu1iPebk2XE4ZjpR6f6bkYri8N3hjeOkkOWZc2xLMuUe2R/xhizn6SHJN1tWdbNQQRfbWpqpA7aWH7CGDLG7uQ3RwU1w8CB9nNm9O7QbhkpNc2AAfZznz6tV3plbKXySaRsdXW5J7WZX1gq5XufQw4VKwIdtFE1ssJp+19C0aKQf2XhRwGscBzYNvtXtiLxOd0F1zUnhxoaXCyz/OKLB9G+vT/bPivAUjG4btm03XbOY3BYNjKTlWt1YFnh5vgtD3eXx/W2siD6y4nbd4yDINeJ23lvvbX9nGlx6/dtZYXKerFWLEnSrp19vpEtzt+nWLnwK+Y47Od+xJA5dGVaajs9hhcT1HqpNK58ce0TrNr6vclckyVBta37MAUynoMxZldJT0h6WtLFQSwDLpWrLeNUm0qaMUNatUraf//c1wvu5GHFfs019k8wHm+FKhdmoe/2wgvS0qWlp12kfbRGXTXgL/OLzjs7OVTsIJrGCrRoP0sl7+GStzL3ve/ZPT96vJ2qe3fp2WftX3yvvNLTLIpa/6X9nN1yqA2v+1nm52mHCdVKFuXbfHzvcMpBFexDyyFP37eK+hyKWzxJ5nRdRr3O+/eX5s+XRo2y//fScmjJkpwBAwsq9Kt7tRwzo96Gbnjpe8jL90vSOsl28cX275hObl+uVtV6W9k779inU17HYKk0lrlzi3dcHsTyvKLVYeV8Tw4ZYxplJ4W+kHSHpL1N65b53LKst/xeZizFtTQWiyuIeGtqPN9KU1cnHX20w4mDOEMrtD7q6gp0wBKsXr3sR0ahsPbR/9p/7FB8PlG1HIrrbpCvTZxBXP3W1eWesXkot4ccIv31r1kv+HRb2Vdftb2trBzH4XtIDlXCcVylVo6TZgcebz0rWgWHmRzKmrCS5SZl/45CtSQO4qRYeTvssNa/vbQcyu5/p9wyq/EHlTh+j/z17kdrLTf1VRTrxI/6tF27yrvzC0tQxw9jwj02hXVbWaYDdK/zqjSGY48N9xLSL3Gs3+IuiJZDu0hqav57Qd57v5M0OoBlxldcSuWIEdKFFwbyi3hRf/iD3VLCR75UQE5mcu+90g03SAcd5MMCnXESltfiVCM7SVcqV1dy+Y29pVX2nz4MDmaP+PTBBz7MyB9t+hyqxK9+Jf3jH5XNw4FSHVI7KidZI709dN863fXTTsGMRpLpnyvg5FDo97yfeKL0/PPS97/v+U7VbMfrMZ2r2Zp+23l69llvIbVZRkA9scblsOa3M8+URo6MOopohXHy/9BD5bsLK1TGZs60b0cp1Q+aXx1Sl4qrGm4rKyTOF3iVdAJujHT11W3GMEDEgtx/rrzSvvvgoouCW4YTcWhl6Fe/aU4/E5d6MQ7rPql8Tw5ZlrVQ8mFM3qSL21G2XTvpzjuLvx9EvMOHe/6oq/vLg4h9222lWbP8n69P3H7lilslnHuu9IPVkqRx4yqblaTQW2AVE8huGvDPdjkxV3LUGz9euuQS6V//0o6Dtuj22x1+zu0yM8mhzHBdASyi3GcC2c4dOkizZzcvvPzk5S5uOmijZmui1Pu8ymMLWFxvK6vUsce2vYD0+h2rbd1kq/S7nXyyt3k3NrbucsX41SG1k/okiDEGUFilZe4HP3A3/yj232quM4KWfytZly7SnDnhL9vNewgWySHvqmBAziqz776l2zjn2UNLNLLvssqXG7MazFEXLwnf4UsNlev2s+VkkkOlPld2nl272vOKV1Fx5bLLyk9jZLV2YBGBXXf+qk1fW2UF0ROvX8vwkByqhOsWRCEV6Kj7PvDzQ3FNDoUVT9y+N1qNH28/7713MPM3Rrr2Wvvv/A78/dbY2PpjjJcLnD32iL4l3NVXe/uc02QN+2JyBbXtYvLbY44oExRnnGE/uz6vzOM0CRaXfTIucSRRIB1SowIvv+xq8iXW0GDiSBLXzWiirTHanPS4yHJ5/hXbQXKorH1H2r2JeRCXzP3MmcWHEs+RGS4vCFddZfe4XsSfX1jjaEgIy1LZFeulDxrfeUgOVRKO4+FaI6oHglxsWMkhHz4aK27rp7jUZ3EQtzJw1FHBb5/LLnP2Q0OlPvqoss8vWeJLGJ75uR1ctSb3cf5hikMMYQpiPz3rLGnYMP/n61UQrVfclpNDD/Vn+Um7rQze0XIoaAX2klgeAGIZVKtMR7kHHJD1YvvmcVh79w49Hi+KHSQq7uPGybIdJIcKFYE9278hSerbY0NL91GZIVIdLTfexaplGOT99nP4gTCOem5W2l572c99+hSfpn//yuIpwnESJvMTfmNjIHE4iiEkpbooq6TPDKfCWgdxPfmLQxno3t1+3mefaOPwKg7rME4y6+PAA+3nkBpAFo0jrfLrT7/XR7Gh1eNa10EaPLj179des58feiiaWIoJIjkUZqKnnIMPDn4ZXnFbmXckh4IS9V7hVkziLRZG587SG29IDzyQ9+Irr8iPXnQ/+kh6772KZ+OKl1XutZLz2nJoapc79IqGa8+Ba3TddfbqHjrUWwxx1KOH9Prrdt/MGYEn68pteIcbyRjZnaa/+qq0yy7FJ3ztNendd73FUkF8Le64w/4Je9ttA1tE1IyR3nrL7me91DReva/t9I+TLnYUhyM5bcDdxxPX28q88rPPiP797eJ+xx0VhRS5qG8hiFsd8POf2+cgmR8Uwhbk+ohqXa9YIS1fXvi9MPsAeuMNqVOn4ObvVLXUp05V+n1feske3l2yT4Ok1obKcVFNCYpC2+vxx6U336y+svvmm1FHEC1uKwtK5khToFfEQCuJLl2kNWsCXEB02uaAjLdOrwvUYgE3aiio5QLLQ+dJfnVInem475NPCs+zRls0VPZPMu3auV/dmRFj4nDiVcyQIQ4mCuJquMKKwLJk1y8F2lDnzLpbN/tRaQyZusztxuzQwVU/atkqzV05+no+bdPsXzFLLcbL4rbTh1LPze4/6GAFWB5+I6q25JDbFpXlvrfH4h4rjvr9k/0bjRROy7goNTT48jsUsvTt63zazGGnrs7/ONiuydS1a0tXmLFNvmTueijWMi3ptt667W+Tme8cVSvLjEoSc27qpmpEcigoDzxgN0cYMSLc5T73nLf+UqrlLD8B/FjVfnVIbVnSb35jZ/8LdnNT4VXgDjtIP/yhdMopDj/wu99JX/e0qBy/+Y305ZfePlsyWReD28rcbApf+xzafnvp5ptLDzWEoira76+9Vpo82bdYclQQV/53mj9fOvxw7/N78UXp738vP93rr0u7714+HrfoU8H9OnzySemXvwzs7lU0i11fZSG74w77Vs2jjw5neYxWlixRjhxY6ngwcaL94+uVV4YXT1Ccls8rrrBHjfy3fws2nnKqqdVW2EgOBaV3b+nf/73gW4EW1AEDvH2Oo1IiBNEh9de+Jl16qfeYSi7X2AcKxzIdO1To0EN9mU2rhO4fvtc1l1/u8wxL8xK/41teQt6mFS1u2jS/wmjDS1jFtsthh1UUiuO+v4YMsXOVy5ZVtjwU5/S2sm23tTtn9ut2joRWtYFL+wVOt27S1KnhLS/t6xv+aN9euu66qKNoy0s96/Qz9fXx/M5upP04VOUNgZE0N9xgN1Pfccfi0/TqZbdyue02jwvJ2+vHdvuDp9ncpO9qDy3xGESrWzRZ3bsH24yxRvbPKq5bHF071W4PG+HQ7mGLvM+hKHg4E540KYA4fBDXk3o/N/tsneuspUb+Qi+9VDriiIqX79dtZcccY9+teP75FYfkSzxey87hh8d3f0iqqPfjqJcfJiffdf/9pWuu8WdeXqRhKPtq+i5hi3J/jXK7nXtudMtG9aLlUARieQCISVBHHCF99lnpadq3lz7+2L9l/nLnqZLmu/7cd3WzvqubJQ99BmU7Xr/S8f+saBZlFWs5VHazjxolrV8fTFBJ4nrFuZhXgILcrffeW9p3X2nRouCWEcgtdBFx0+dQuSJyrubo3GX3uJ/Rrbfaz5980jYwF/xKDjU1SZs2efusl76AglrO00/7v9ykifv+51W1fq9CSn3XF16wn6dPDyeWfMX6tAo7GRWkNJU1v0WZHIpq2bfeGlyr/0KSVj65rcw7Wg5FIJYFNWl7fYWG65WWv9vXfBXKMv1YxZkO3k44weWym5sZuC17sSyrAcvpc6jYRgtyxWR68wtTyvb/HCF998zFTVz2qQNkX+3tUu9+mMbM8LWDBvkZkTvHHtv2tW98o7J5FioKe+3V9jU327CpyXs8UUhzVVBIXPbXMMRpiOxy8w26nI4bZz+3bx/scuCPat5P99476giSieSQdySHkD7G6EXtr5Xqo3+otzqYjVFH5NhWW0n/+Ic0e7aLD/3znzJPPiGJSjIWyp3VNjQ4mg3b0rmC6ypJfQ4F4Cz9RB+qSSM7ux+z9eKL7SGooxzl5+abczuvXrlS+o//8H85Rx9tjy/hxerV0tKl/sYTB2nvHBnhC7tc3HOPfa4V5ohLlP1kCnq7LVzo790SUrB9DkUtP07Old3jtrIIxLKgJmWv90m9vlS9MsNZlejgKIZ693b5ge7dZdbYf8ay7MVMTp9DXbrYzz165E5Uyf7Svbv9nBmD1SU/bkuq/AO5/K4+Ghvt58zwxW5UWsa9LNONWOyDll3Km7RCMtu6/rgxUr9+/oflRm2ttM02rf/36eN9XuW2idfv2q2bt8/FQdSnBLHYTxT9eghTFCOp5uvXT1qxou3rYW+HujoP51qITBT1Raaf0KAbe9fXR9OgPF+a6sK0IzkEG3t96MI8mHltXul3sVi82PsQ85H41rekL76QzjrLv3lecom09dbShAkVzcbJtgxrt7YsSb//vW9t8GfMsEekOuooX2aXsx5e1TB98fxr0ti20z3+eOEh0v0Qt9vKIL3zjvTee9KsWe4+l8bDZdrKbdq+b6X82ideftk+Twhq/mH685/t1kcIXhT769y50vHHB3fOgOgksb7xE8khpE8K2xzGpaLbc8+oIygvp8+hmhrpvPP8XUBtrTRxor/zrJQfBcTHEe3q673nzsp9lWFaIh1Q+AOV9llTSmYxW7YEtwxP4lI5RGDHHe3H3Xe7+1w1HzKcFIcwOgOPS7GMSxxx59c+se229iNfsQ6p42zXXe0HghdFndy1q3TmmeEvNypJqwvpc8i7BFa3CETS9no/RVRzGCO7RcrDD4ezLFFJVixGKzBOu2ycYnG8iQIO+n/+p7VT0+zFBV2EvvUt6Vdbfcv5B+K08SISo906tsJeR3HZJnGJI0hXXSWNHCmdfHLl8wqrQ2oEK//4FaQ07GPVIGn7YNLijRNaDkUglhUhe1FgamulrwoMiGZZkn5yfygxeL0wjWVZDVhOn0MxVmrbhN3nUByUqsKi+HpHHmk/MsJKDv3iF5I6/1r6ssTCsl+n7m/hdFWwyoLDug1f//7SSy+Fu0y32zns0crSLv/4FXdVcAqDgFA23KPlEIIX1s8PTvl1VuFyPPnMYqM4qcm+MD311PCX70afPp77ag5eDM5I3YQQg3ADN3x48fdKfv+IRiuL3YnKaaf5Psu43TUJ54rtFmElzkaPtp8PPbSy+fglDXWon5zWb1H3fyhJnTu3diqM6MShI3SUl7S6MLbnXAlAy6EIxLKgBrXXb9oU/5vFvW6QRx6JYQcihWVXkt/5jnT99dKqVc4/F6bly8NfZracPofyxXLnLS4ut1gF6Y9/tL9nZoSb7O8cp80VyYlKse2aCaJbN+n8831d5KZNUrt2vs4yNEFd2FYDp43NKl03++0nbdxojxaF5Aj6EBLE/Fev9n+ecC+N9SkQZzG/akdogjqy19ZWb3Kopsb+fgmQPVKSMfE+8W7XLrkXl2HihMpWU1O+vMShBVEkyaFyC6ur8/3719YmOteIZpXe9lOJOB+fojJ0aNQRlBZ0S6Ag6hTONaoH50PBS9NxPU3ftZBkXNkiFK++KvXsGXUUIYjodpIo0bzSuaj6HFq2TFqzxt95Oi57VVQwPO1vf/qTfY9BgEIdyt7pSqii7e4H+hxqlV80KCrR+vWv7aHRDzww6khKC6rqiftvjPCO28qSIWnHPa57vCM5BJsxGjYs6iAQlPxKksqyLVcHvgCOkv37248IQ3AtKeWobJy77x54DKGeqCRlwyB2Kq1X4lAv+SFuu1C3btIBB0QdRXlBrbdqKVdoy48yE7f9FdEjOeQdyaEIxLKgpvnIG8sN4q9ilWSpzT5pkjRkSHAxxY2rYhCDMuNrCBXu/3GqPuI+EJeT7ebbti23AuK4ghIgBrt/4Cgatrith2efld58M+oo2nK6nv7t36S77mK0MsCrSo4/r7wiPfGEt8+yz6UHySGkT0Q1XJQVa7l+aQu55ZZgYom7kh1Sx4CTcpSGi9d8nvevkHbMUG8ry3AylD2AxDjkEPuRVCNH2skhqiBkcFtZeIYPLz3CazXIL0+UDfe4ixc2UsL+a2iQLrig6NthVlj5LYeStLl33lk6+ujwlueoz6EYrEAn5ScGYcZXRH2PhTLAIZ3nBIrV1hbrJN24AINX3FbmTlR1bZrq+DR910JoORSBWFZiad4Tgtoga9faz3NyX45iVSe5z6G33w5nOUnZBZISZ1TiWrbd3P/ONg5XXMtMFJy0MqV8ohjy0nDKzzJAHR68pO2z9DnkHS2HkD4Rj1YWRUXlpc+htEnKASQpccZeRPVAKH0OHXyw/cyY4K4wyFurUusiDd8f3jgtG5Qh+FkG9t3Xv3kBaUdyCDayBKELc5WTQXcu7n0OobS4VmWh7oMPPii99ZZ9a2shVAQFsVpaeV0Xcd3/3KIsuFMt2x3JdOON9nPPntHGEQbqJme47vGO5BBsaT6yR1RzRNHnEMpz1OdQhOK0LeN40HUdU0grtFs3+7lduxAWVl8vDR5cfro4FaYEqebVVul3i2OdgPip5n0IzvhZBjKNZKl/kEFyyDv6HIoABTViEZ+VRLH4SEZKShhOVpMt7tvvF7+QHnhA2n33qCNRVVUEP/mJNGyYP/OKexkKU6l1Ueg91l26hV2lUN6Sz88yk6ZEAGUfQSM5BFuaa5uQjiZxGMo+lJGSEspRMYjRmUepUGIUZiwUXB8h75C9e0uXXx7qIsurgnr/zDOjjiAdsvehQvtTtdY5VbCLhIr1hShQ7pDhR6Iw7eWJ28oiEMuTqLTvCVUuTb+qVCrufQ652VXD2q3jVH1UQxk/7DCpU6eoo0C+piapY0fptNOijiS+4lQXIHzVUP8iHH7WFZzjIh9lwjuSQ0ifkIcPi8PJMpWkcyX7HIrBxozjNoxjTNlKbrYYbNN8vXtLn38edRTIN2iQ9MUX0oABUUcSHCdD2adB2r5vpWJYjSLmuK3MmzR8Rz+kqUz4jeQQbBzZq1rI+bBEcrQLVNuKO/lk+7l9+2jj8BFVGfzWv7/9PHZstHHERak+h045JdxYkCy77GI/H3RQtHHECfVK5UgEIB/ngt7R5xBsadqLIsqURHnQKnbgTNNmrxZOtpnjsnb33dKMGVKHDoHHFDuJDNonnEG7MmCA9I9/SI2NUUcSXzU10scfS126RB2JP9JcPQRp2DBp5Uq7dSRsjz9ut0hMG24r84a6yZ00lAm/0XIoArEsqGmubZqaAp19HJrpFztwxrIsRiSV66K2VurZ0/PHM7tOQ4NP8fggTZ3lVizN9X4WJ+Wjd+90rC4n37HYND17tg4pnXTUGcHp06fyfama9sX27aXu3aOOItmqqTygMr162c/sU97Rcgjp9uCD0hFHBLqIOLTWyU8OcSCtUAxWYBwuXu67Txo3ThoyJOpIGF7blTgUHsRaqX2niu5CLYs6BIi/NLUcSsN3rMSVV0p9+0oHHCBNn+5tfaW93qflEGxp2hOyv+spp0hdu4a6+CgqdvocKq8ad4Ggv1OXLtKppwa7jMBV44aHKxSB8jLHjLTcDsQxEkiONCWHMjhuFVZXJ51zjn2rM7xh1UXgiiuijqAAapnAFFu1Ya5y+hxCWmSX8aSfKM6aJV1zTdRRVL+kl5MwccwAEDdpTA7BGcqEe9xWFoG+faOOAHEQZoWVyaBTSRaXlHXDxZl7BddZAlbk+ecHvIAErIMwsTpYBxmsByA50niOm6bv6gUJQ+9oOQRbms6EIvquUfb3k1nmli3hL7uqHHmk/XzAAdHGgaIK7V+cHORhhcClzChkEydGG0dY2EXc6dfPfh4/3t/5nnBC6ffZTpDSlQhI0+UaokHLIdiobQITh1XLaGXlOdpOY8ZImzfH4mbmUtsuzds1+7s72qZx2EGjkubvXkCa95t8+UWjocGu+tJWZNL2fb3q3j2Y8jFvXuH9ku2CbGlKDqXhOyJaJIeAgMWhIs8cOEeMiDaOqhBxYsjNSXGaTqA9f9c47KBhq6+3n/fYI9o4EFuFdosY5MRDl8bqwasgyocxpev2NB3jUFyakkMZlP3SKikTaV+3JIdgS9OekKbv2swY6aWXpJ13bvs6bEk5qUhKnLGX5sLfs6f0/PPS0KFRRxIraS4SGawDAEmTxuQQSqNMeEdyCDbOCAMTl1U7cmTUEQDhcnRSkNYzB/rNQglxOW5FjfUAxF8a99O0nro4lcYy4ReSQwByPPaYtHZt1FGELykHEidxctIAOMf+AgDR8LP+TUNdnpRz1bhIQ5nwG8kh2NJU20Q8Wlmx/8NUatnHHRdaGECg0lStAX5gn7FxQZEMbCdI6bqFKA3f0Q8cy7xLYfeCKIi9KDD5q5ZVDT9wglAhdkQ0oygAycI+Wz382JZpSg5lsA8gKCSHgJDF4eDFQSW5GK2sNNf7Vxx2SESKIgAAyZXG5BCcYbQy90gOwZamPSHk73rYYfZzbd5NnGla5UDQ2J9QSseO5aehDCEfZSLe2D6QpJrmq9nDD482DsQHCUPv6HMINo6wgXnwQWn5cqlDh9zX49rnEFBtKO/44ANp3brS01BOOBVAacuXS1ttFXUUQK6aGundd6W+faOOJDwcr0ojOeQdySEgYPX10g47tP4fp5PvOMUCVKJdO/u5e3eHH6Dwp0r37i7KBtg9UFC/flFHABQ2aFDUESBOOIZ5x21lsKVpL0rTdy2DjHpyldp2adyujY3Sj34kPf102/fY5eEE5QQAkAQcrxAUWg7BRi2TKmzu5GLbFXfBBR4+lMZMGgBUAapvAKVQR7hHyyEgZIcdJvXqJV1+eXQxUFkCAPKRfLZxjIw3yimAUiqpI9Jev9ByCLa07wkh6tVLWrUq6ihsbPbqxva1caEHwAvqUCBYHJ+9Yb05w3pyj5ZDsHEGBFQNDoa2ktUadR4AJBrVOIBCGK3MO5JDAICq5OikgDMHoAUX2wDCRJ3jDeutNNaPdySHgBQaMMB+rquLNg54R07DOU4SUAr7EgBEg/oXiJdAkkPGmLuNMUuNMV8YYz41xjxvjDk0iGXBJzXkCdPk8celRx+VevaMOhK45SbRQVLExsknnGB/aZX2dUGdkQxsJwClUEe4F1RGoF7Sf0o6XtLpkj6R9JQxZt+AlgfAhZ49peOPjzoKIFj0OQQ3OIlEPqqJeGK7VA+2JYJQSZ9DaS+TgSSHLMs6y7Ks/7Qs6zeWZf2PpBMlrZR0ahDLA4BKXXyxNFZP6QLNijqUko47TjrqKOmGG4pPc++9dvJvzz1DCyu5yAgALdJ+UpyP6gEIFvuYO1ddJY0dK511VtSRxBsdUnsXylD2lmVtNsaskdQ+jOUBgFu9eklP6ciowyirY0fpySdLT7PLLvZtgyiBq2DkoUgAAOJsm22kp56KOor443juXWAdzRhbrTGmhzFmkqQdJN0X1PIAAKh6Y8ZEHQGQGlxgxBvbJ/nYhghCp07284UXRhtHEgXZcuhkSb9o/nutpJMty/rfAJcHAEB1e+aZqCOoWjQ/RwZlAQgH+xqCsNVWlC2vHCWHjDFdJG1TbjrLspZm/Ttf0l6Seko6TdJDxpgjLctaWGD+50k6T5K22247JyEBAFA5zh6AFvyKjySh+k4u6hrESmGUFAAAGwNJREFUVdrLptOWQydKusfBdC2r07KsTyUtbv73aWNMX0nXSzow/0OWZc2WNFuSRowYQVUPAAhW2o/+aIMi0Srt6yLt3z/u2D7JR2IPiCdHfQ5ZljXHsixT7lFmNq9JGlB5yAAAVKhDh6gjAGIr7Rduaf/+ScF2AgB/hTJamTHGSBop6b0wlgcgWnffLe28c9RRoKD77pOamqKOInrPPCM9+KDUp0/UkSBiXGC2okVGLtZHPDFMdfKxbwHx5HtyyBhzgKTJkh6T9IGkHpLOkrSvpGP8Xh6A+Jk4MeoIUNT48VFHELqCFxA77ihNmxZ2KEAicOGGOKN8Jh+JPSCegmg59KGkzZJukNRL0seSlkja37KslwNYXiL16SOdemrUUaTE7rvbDwCpkqgLiJ49pXPOiTqKVEpUOUEovvtd6YQTaAELIN0uuUSaNy/qKBAm35NDlmUtk/RNv+dbbVaujDqCFPnTn6KOAEAEEvXL5McfRx1BaiWqnCAUxx9PuYgzErrJxzZMhttusx9pkvay6ahDagAAkirnQF/DYQ+Fpf2EUGIdIFlI4CUX2w6Ip1A6pAYAIHIrV0rt20cdBRBbXLAhCUhiAkAwSA4hvRjKGkiFlgteRiYDHOHiG0lAMjO5qGOAeKJ9PdLpgQekN96IOgoAAeLkEwCqD0PZA0AwaDmEdDr99KgjAAAAgEsk/gEgGCSH4K9rr5X++teoowC8mTJFWr486ijgE35VBgAAgFNpTz6THIK/pk2LOgLAu+9/P+oIEIC0H+hRGklEIFmo0wEgGPQ5BFSDr3896giA2OLiHwCqD3V7ch14oP28zz7RxlEtevSIOgJUC1oOAUn3r38x8hpQAL8uwwnKSSsutpEEdEidfEcdJa1eLXXrFnUkybd2rVRDcw/4hOQQkHRbbx11BACQWFxgtkXCDHFG+awOJIb80dAQdQSoJuQZAQBA6nHB2YqEGZKAcgoA/iI5BAAAUo8LTQAA0i3tPxSRHAIAVKXZs6WTTpL22y/qSIBkSfvJMQAAaUSfQwCAqrTDDtLDD0cdBZKChAiQDHRIDQDBoOUQAAAAgEQgkQsAwSA5BAAAUovWB0Ayse8CgL9IDgEAAICLbSQCLYcAIBgkhwAAQGpxoQkkE8lMAH5L+zkBySEAAAAAiZD2izcACArJIQAAkFq0PmiLi28AANKH5BAAAEg9EiJAspDYBQB/kRwCAACpx4Wm1KmT/fzDH0YbB1AKiVwACEZt1AEAAAAgerW1JMmQHJRVAPAXLYcAAEDq0RoBSIbMvkpyCIDf0n4uQHIIAAAAQCKk/eINAIJCcggAAKRWjx7281ZbRRsHAABAlOhzCAAApNZdd0n77CONHh11JAAAANEhOQQAAFKrSxfpoouijgKAW/Q5BAD+4rYyAAAAAIlAh9QAEAySQwAAAAASgQ6pASAYJIcAAAAAJAothwDAXySHAAAAACQCLYcAIBgkhwAAAAAAAFKM5BAAAACAROG2MgDwF8khAAAAAInAbWUAEAySQwAAAAAShZZDAOAvkkMAAAAAEoGWQwAQDJJDAAAAABKFlkMA4C+SQwAAAAASIdNyiOQQAPiL5BAAAAAAAEilffaJOoJ4qI06AAAAAAAAgCjMny8tWxZ1FNGj5RAAAACARKBDagB+69JF2mOPqKOIHi2HAAAAACQKfQ4h6T7//HOtWrVKmzZtijoUVIm6ujo1Njaqc+fOnj5PcggAAABAItAhNarB559/ro8++kj9+vVTfX29DE3iUCHLsrR+/XqtWLFCkjwliLitDAAAAEAicA2NarBq1Sr169dPDQ0NJIbgC2OMGhoa1K9fP61atcrTPEgOAQAAAEgUWg4hyTZt2qT6+vqow0AVqq+v93yrIsmharXLLlFHAAAAAAAogBZDCEIl5Yo+h6rRypVSp05RRwEAAAAAABKAlkPVqE8fqWPHqKMAAAAAfEWH1ED0xo0bp4EDB+rLL79s897hhx+uwYMHa+PGjZo2bZqMMSUfo0ePliSNHj266DTLly9vmf+LL76oMWPGqFevXurYsaN22GEHnX322Vq+fLkWLlxYdnm02CqOlkMAAAAAEoHrOiB6t99+uwYPHqwbb7xR1113Xcvr8+bN0zPPPKMFCxaoffv2mjBhgsaOHdvy/p133qnnnntOjz32WMtr2aNqHXTQQbrhhhvaLK+xsVGSnRgaPXq0jjvuON17772qr6/XW2+9pQcffFDvv/++hg8frpdffrnlc7///e91+eWX69FHH9U222zj6zqoRiSHAAAAACQKLYeA6DQ1NWnatGmaMmWKzjjjDA0aNEhr167VpEmTdOaZZ7a0BmpqalJTU1PL5+bNm6cOHTpo3333LTjf7t27F31PkmbNmqXBgwfrkUceaWkBNGbMGF1yySWyLEvGmJzPf/LJJ5KkYcOGafvtt6/wW1c/bisDAAAAkAi0HALi4ZJLLtFOO+2kiy66SJJ03XXXad26dZo5c2Zgy1yzZo0aGxsL3hrG7WKVIzkEAAAAIFFoOQREq7a2VrNmzdL8+fM1ffp03XbbbZoxY4Z69erleZ6WZemrr77KeWzevLnl/eHDh2vBggWaPn26/u///s+Pr4EsJIcAAAAAJAKNA4D4GDVqlL797W9r6tSp2muvvTRhwoSK5vfoo4+qrq4u5zFw4MCW96+44gqNHj1aU6dO1cCBA9W3b1+df/75+stf/lLpV4HocwgAAAAAgGhdeqm0ZEk0yx46VLrtNk8fveKKKzRnzhxddtllMsZo8+bNsrKa9tXWOk85HHzwwbrppptyXuvQoUPL3507d9Zvf/tbLVq0SE8++aSef/55zZkzRz/72c/0/PPPa/jw4Z6+A2wkhwAAAAAkCreVAfHQvn37nOeBAwfq/fffb3n/vffec9wZdLdu3TRixIiS0xhjNHLkSI0cOVKStGTJEh144IGaPn16zihocI/kEAAAAIBE4LYyVC2PLXfi5oknntCGDRta/u/bt2+gyxs6dKjGjBmjt956K9DlpAHJIQAAAACJQsshIJ6GDBkS2LxXrVqlxsbGnNcsy9Lf/vY39e7dO7DlpgXJIQAAAACJQMshoHqtXr1aixYtavP6rrvuqk6dOmnChAnasmWLxo0bp4EDB+rTTz/V3Llz9ac//UmPPPJIBBFXl8CTQ8aYSyTdJum/LMv6ZtDLAwAAAFDdaDkEVJ8FCxa09CWU7YUXXtD++++v73znO7r//vt1/fXXa+XKleratat23XVXzZ8/X4cddlgEEVcXYwVYsxpjGiW9I2mTpOedJIdGjBhhLV68OLCYkCvz6wsHWAAAAMTdypVS375Snz7230ASvf322xo8eHDUYaBKlSpfxphXLMsq2Ot30C2HbpT0pKRtA14OAAAAAAAAPKgJasbGmL0lnSTpqqCWAQAAACB9aPUOAP4KJDlkjDGS7pT0Q8uyVgSxDAAAAADpQofUABCMoFoOjZfUW9LMgOYPAAAAIKVoOQQA/nLU55AxpoukbcpNZ1nW0uZpb5R0kWVZ6x3O/zxJ50nSdttt5+QjAAAAAFKGwVQAIBhOO6Q+UdI9DqYzkq6W9IGkZ4wxXbOWU9f8/78sy9qc/SHLsmZLmi3Zo5U5jAkAAABAinBbGQAEw1FyyLKsOZLmOJznTpJGSPq0wHufSjpA0osO5wUAAAAAAIAABTGU/fck3Zb32m2SPpN0raQ3AlgmAAAAAAAAPPA9OWRZ1p/zXzPGrJH0iWVZC/1eHgAAAIB0oc8hAPBXUKOVAQAAAICv6JAaiN64ceM0cOBAffnll23eO/zwwzV48GBt3Lgx5/Wzzz5bxhgZY1RTU6P+/ftr/Pjx+uijj4pO09TUpFNOOUXLli3LmdfChQtbpst/TJgwoWW60aNHt7xeV1enxsZGHXzwwbrrrru0YcOGgvP8859z27p8/vnnmjp1qnbZZRfV19erU6dOOuCAAzRnzhxt3rxZ22+/fdFYMo/7779fy5YtK/r+oYceWnAd1NbWqkePHtp///01Y8YMffbZZ663lRtB3FbWhmVZo8NYDgAAAIDqRYfUQPRuv/12DR48WDfeeKOuu+66ltfnzZunZ555RgsWLFD79u3bfG7nnXfW3LlztWXLFr355puaMmWK3nrrLb388suqqalpM83SpUs1ZcoUHXnkkVqyZEmbef785z/XgAEDcl5rbGzM+f+ggw7SDTfcoC1btujjjz/WwoULdeWVV+ree+/Vc889p65duxb9nqtWrdLo0aO1Zs0aTZ48WXvuuac2bNig5557TpMnT1avXr302GOP5SSaxo4dq29+85s5SaqBAwdq7dq1kqSZM2dqv/32y1lOly5dCq4ny7K0evVqLVq0SDNnztTs2bP13HPPafvtty8acyVCSQ4BAAAAgF9oOQREp6mpSdOmTdOUKVN0xhlnaNCgQVq7dq0mTZqkM888U6NHjy74uY4dO2rfffeVJI0aNUr19fU644wz9Morr2ivvfYqOE1DQ4NOOeUULV68WKNGjcqZ3+67767ddtutZKzdu3dvmZ8kHXvssRo/frxGjRqlSZMmae7cuUU/e8EFF+jTTz/V4sWL1a9fv5bXx44dqwsvvFCfffaZhgwZkvOZ2tpaNTU15SxTUktyaKeddmrzXr7sdSBJRx11lCZOnKi9995b48eP14IFC0p+3ituKwMAAACQCLQcAuLhkksu0U477aSLLrpIknTddddp3bp1mjlzpuN57LnnnpLU5raxbHvssYck6cMPP/QebJ7dd99dF154oX7+85/r888/LzjNsmXL9Nhjj+nqq6/OSQxlbLfddm0SQ0FqamrS1KlTtXDhQi1dujSQZZAcSrlzz5VmzYo6CgAAAKC8bt2kQw6RHn446kiAdKutrdWsWbM0f/58TZ8+XbfddptmzJihXr16OZ5HJinUp0+fotN88MEHkqSvfe1rbd7bvHmzvvrqq5yH5bBZ4ZgxY7Rp0ya9+uqrBd9/4YUXZFmWxo4d62h+Tm3ZsqVNzFu2bHH02TFjxkiSFi1a5GtMGSSHUm72bOn886OOAgAAACivpkZ69lkpq/9WABEZNWqUvv3tb2vq1Knaa6+9cvrZKearr77Sxo0btWTJEn33u9/Vtttu29KCKHuaTZs26fXXX9dVV12lsWPHau+9924zr6FDh6quri7n8ZOf/MRR7E1NTZKU0yF2thUrVkiyWwj56dhjj20T8/XXX+/os+VirhR9DgEAAAAAEKFLL5WWLIlm2UOHSrfd5u2zV1xxhebMmaPLLrtMxhht3rw5p/VObW1ryuGVV15RXV1dy/+77bab5s2bp4aGhqLTDBgwoGgfOw899JAGDhyY81qhFkaFOG1hZHy+l/XWW2/V/vvvn/Na3759HX3WacxekRwCAAAAAACuZUYQyzwPHDhQ77//fsv77733XsvoWoMHD9ZPf/pTtWvXTv369Wszslj2NJs2bdKLL76oq6++WhMnTtTDBe4l3XXXXct2SF1MpmVQ7969C76f6Wfogw8+0KBBgzwto5BBgwZpxIgRnj5bLuZKkRwCAAAAACBCXlvuxM0TTzyRM7R7dquYhoaGsomR7GlGjhypL7/8UlOnTtXkyZO1zz77+BbnM888o7q6uja3tGUceOCBMsZo/vz5viaHKvHMM89IstdLEOhzCAAAAAAAVGzIkCEaMWJEyyPTosiryy67TD179tRNN93kU4TS66+/rrvuukunn366OnXqVHCa/v376/jjj9cNN9yglStXtnn/ww8/1BtvvOFbTOUsX75c06dP10EHHaSddtopkGXQcggAAAAAAMROQ0ODJk2apGuuuUbvvvuudthhh5b3Xn/9dX3xxRc503fp0kWDBw9u+X/16tVatGiRtmzZok8++UQLFy7UPffcox133FG33HJLyWXPmjVLX//61zVixAhNnjxZe+65pzZs2KDf/e53uuuuu/TTn/7U9XD277zzjnr27Jnz2lZbbaWhQ4e2/L927VotWrRIlmVpzZo1eumll3T33XerU6dOmjt3rqvluUFyCAAAAAAAxNKFF16om2++WTNnztSPf/zjltdPO+20NtMecsghevbZZ1v+X7BggUaOHKna2lp17dpVQ4YM0YwZMzRhwgR16NCh5HIbGxu1aNEizZw5U/fcc4+mTJmiuro6DRs2TLfeequOPvpo19/l8ssvb/PawIED9de//rXl/6VLl2rkyJGqqalpSXZddtlluuCCC9SlSxfXy3TKBN3jtVsjRoywFi9eHHUYAAAAAAD47u23385p3QL4qVT5Msa8YllWwY6f6HMIAAAAAAAgxUgOAQAAAAAApBjJIQAAAAAAgBQjOQQAAAAAAJBiJIcAAAAAAABSjOQQAAAAAAAhituo4agOlZQrkkMAAAAAAISkrq5O69evjzoMVKH169errq7O02dJDgEAAAAAEJLGxkatWLFC69atowURfGFZltatW6cVK1aosbHR0zxqfY4JAAAAAAAU0blzZ0nS3//+d23atCniaFAt6urq1Lt375by5RbJIQAAAAAAQtS5c2fPF/FAELitDAAAAAAAIMVIDgEAAAAAAKQYySEAAAAAAIAUIzkEAAAAAACQYiSHAAAAAAAAUozkEAAAAAAAQIoZy7KijiGHMeZjSe9HHYdPekr6JOoggBBQ1pEWlHWkBWUdaUFZR1pQ1iFJ/S3L6lXojdglh6qJMWaxZVkjoo4DCBplHWlBWUdaUNaRFpR1pAVlHeVwWxkAAAAAAECKkRwCAAAAAABIMZJDwZoddQBASCjrSAvKOtKCso60oKwjLSjrKIk+hwAAAAAAAFKMlkMAAAAAAAApRnLIZ8aYXYwxvzXGrDPG/N0Yc70xpl3UcQHFGGMGGWN+bIx53Riz2RizsMA0xhhztTHmQ2PMemPM88aYoQWmK1v+nc4L8JMx5kRjzH8bY1YYY74wxrxijDmlwHTnGmPeNcZ82TzNIQWm6WeMecwY8y9jzCfGmP80xjR4mRfgN2PMN40xLxlj/tlc9t4xxnzPGNM+axrqdFSd5rr5C2OMZYzZOut1yjsSzRhzdnO5zn+cnzUN5RwVIznkI2NMN0nPSrIkHSvpekmXSbouyriAMnaVdKSkdyT9pcg0V0m6RtJNko6R9IWkZ40xfTITuCj/ZecFBGCy7LI2SdI3JC2Q9KAx5qLMBM3Jorsl/VTSEZLelPSkMWa3rGnqJM2X1F/StyRdIulE5d3H72ReQEB6SHpO0gTZZe8+SVMk3ZI1DXU6qtHNsstfPso7qsXBkkZmPR7Neo9yjspZlsXDp4ekf5f0qaTOWa99V9K67Nd48IjTQ1JN1t/zJC3Me38rSZ9Jmpr1WkdJH0v6ftZrZcu/03nx4OH3Q1LPAq89KOm9rP/fkXRf1v81kt6Q9LOs106RtFnS17JeO0nSFkk7uJkXDx5hPST9QNIaSYY6nUc1PiQdKGm1pMtlX/hu3fw65Z1H4h+Szs4u1wXep5zz8OVByyF/HSFpvmVZn2e99pCkeklfjyYkoDTLsraUmWSUpM6Sfpn1mbWSnpBd5jOclH+n8wJ8ZVnWJwVefk1SX0kyxgyQtKNyy+YWSY+obTn/o2VZ72W99itJGyWNdTkvICz/lJS5rYw6HVWl+ZaYO2W3gsiv6ynvSAPKOXxBcshfO0tamv2CZVkfyM7G7hxJREDldpbdUuLdvNffVm65dlL+nc4LCMNItd5KmSl/S/OmeVtSd2NMr6zp8sv5Rkl/U245dzIvIDDGmHbGmAZjzP6SLpY0y7IsS9TpqD7nS+og6a4C71HeUU3+Zoz5qrkvuYlZr1PO4YvaqAOoMt1kN9vO92nze0ASdZP0hWVZm/Ne/1RSgzGmffPFsZPy73ReQKCaO4c+TtI5zS9lyuiavEk/zXr/Yzkv507mBQRprewLZsnu++qK5r+p01E1jDE9JE2XdLplWZuMMfmTUN5RDVbK7gPofyW1k93n4d3GmAbLsm4V5Rw+ITkEAEgVY8z2svsbetyyrPujjQYIzChJDZL2ljRV0n9K+k6kEQH++4GkRZZl/TrqQICgWJY1X/ZgGBlPGWO2kvQ9Y8ztEYWFKkRyyF+fSupS4PVuav3FGEiaTyVtbYxpl/crQjdJ67J+PXBS/p3OCwiEMaa7pKckvS/ptKy3MmW0i3J/VeuW936pcv4nl/MCAmNZ1qvNf75ojPlE0k+MMf8h6nRUCWPMrrJbfx5ojOna/HJD83MXY8xmUd5RvebJHhBje1HO4RP6HPLXUuXdi2mM2Vb2gSq/7wkgKZbKbsI6KO/1/PuWnZR/p/MCfGeMaZD0pOyOeY+2LGtd1tuZ8pd/P/3OklZblvVx1nT55by9pAHKLedO5gWEJZMo+pqo01E9dpBUJ+ll2Re0n6q136HlsjuppryjWllZz5Rz+ILkkL+eknS4MaZT1msnS1ov6XfRhARU7CVJn0s6MfNC80X2MbLLfIaT8u90XoCvjDG1skcL20HSWMuyVmW/b1nW/8nunDq7bNY0/59fzvcyxvTPeu0bsvt2edrlvICw7Nf8/J6o01E9XpR0UN7jpub3jpR0syjvqF7flD063/uinMMn3Fbmr7tljwjyqDHmJtm/JE+TdEvekIFAbDRX+Ec2/9tPUmdjzDeb//+1ZVnrjDEzJF1jjPlU9q8Gk2Unl+/MmlXZ8m9Z1pcO5wX47Ueyy/klkno0d2Ka8ZplWRtkl9efGWOWSfq9pLNkJ5NOzZp2nqQpssv5NbKbZ98q6UHLsrJH9nAyL8B3xpinJT0r6U3ZI87sJ+kySQ9blvW35mmo05F4lmV9Imlh9mvNfcpJ0guWZX3R/BrlHYlmjPkv2Z1Rvy67Vc/JzY+LLcvaIslp2aScozTLsnj4+JC0i6TnZGdgV8oeQaFd1HHx4FHsIfteZavIY/vmaYzsC+LlzWX7BUnDCsyrbPl3Oi8ePPx8SFpWrpw3T3eupL9K2iD7VpxDCsyrSdKvJH0h6Z+yb2NoKDBd2Xnx4OH3o7ne/XNz+VzTXPYuklSXNQ11Oo+qfEg6u7le3zrrNco7j0Q/JN0g6R3ZQ86vl/SKpDPypqGc86j4YZoLAAAAAAAAAFKIPocAAAAAAABSjOQQAAAAAABAipEcAgAAAAAASDGSQwAAAAAAAClGcggAAAAAACDFSA4BAAAAAACkGMkhAAAAAACAFCM5BAAAAAAAkGIkhwAAAAAAAFLs/wFMXcDrZgADcwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 画个折线图来看看模型拟合的好坏程度\n",
    "plt.figure(figsize=(20,10)) \n",
    "plt.plot(range(len(ytest)), ytest, 'r', label='Y-TEST')\n",
    "plt.plot(range(len(ytrain)), ytrain, 'b', label='Y-PREDICTED')\n",
    "plt.legend() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T00:15:25.123437Z",
     "start_time": "2020-06-16T00:15:25.118441Z"
    }
   },
   "source": [
    "'''\n",
    "可以看到许多地方，红线会明显超出蓝线，说明我们的模型拟合度不够，再换个散点图来更直观地看一下：\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "593    -0.600472\n",
       "6344    0.968856\n",
       "571    -0.952770\n",
       "3988    0.904802\n",
       "2693   -0.344255\n",
       "          ...   \n",
       "79     -0.504391\n",
       "3927    0.296287\n",
       "5955    1.321154\n",
       "6936    0.744666\n",
       "5640    0.488450\n",
       "Name: Next_Tmax, Length: 5426, dtype: float64"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ytrain\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Present_Tmax</th>\n",
       "      <th>LDAPS_RHmax</th>\n",
       "      <th>LDAPS_Tmax_lapse</th>\n",
       "      <th>LDAPS_WS</th>\n",
       "      <th>LDAPS_LH</th>\n",
       "      <th>LDAPS_CC1</th>\n",
       "      <th>LDAPS_CC2</th>\n",
       "      <th>LDAPS_CC3</th>\n",
       "      <th>LDAPS_CC4</th>\n",
       "      <th>LDAPS_PPT1</th>\n",
       "      <th>LDAPS_PPT4</th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>DEM</th>\n",
       "      <th>Slope</th>\n",
       "      <th>Solar radiation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>593</th>\n",
       "      <td>-1.376085</td>\n",
       "      <td>1.044566</td>\n",
       "      <td>-0.758505</td>\n",
       "      <td>2.397972</td>\n",
       "      <td>-0.319619</td>\n",
       "      <td>1.403786</td>\n",
       "      <td>0.558225</td>\n",
       "      <td>-0.795155</td>\n",
       "      <td>-0.150445</td>\n",
       "      <td>0.788315</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>0.653021</td>\n",
       "      <td>-0.672255</td>\n",
       "      <td>0.243650</td>\n",
       "      <td>0.372715</td>\n",
       "      <td>0.656413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6344</th>\n",
       "      <td>0.957861</td>\n",
       "      <td>0.669978</td>\n",
       "      <td>0.853678</td>\n",
       "      <td>-0.051662</td>\n",
       "      <td>1.571078</td>\n",
       "      <td>-1.395316</td>\n",
       "      <td>-1.378178</td>\n",
       "      <td>-0.621503</td>\n",
       "      <td>0.355440</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.215593</td>\n",
       "      <td>1.457418</td>\n",
       "      <td>0.158666</td>\n",
       "      <td>1.560277</td>\n",
       "      <td>2.534052</td>\n",
       "      <td>1.302602</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>571</th>\n",
       "      <td>0.315180</td>\n",
       "      <td>0.613097</td>\n",
       "      <td>-0.880037</td>\n",
       "      <td>0.863969</td>\n",
       "      <td>-0.553041</td>\n",
       "      <td>1.445315</td>\n",
       "      <td>2.078433</td>\n",
       "      <td>0.801649</td>\n",
       "      <td>0.847011</td>\n",
       "      <td>-0.301580</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>-0.685654</td>\n",
       "      <td>1.191021</td>\n",
       "      <td>-0.735149</td>\n",
       "      <td>-0.820115</td>\n",
       "      <td>0.596108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3988</th>\n",
       "      <td>0.349006</td>\n",
       "      <td>0.215510</td>\n",
       "      <td>-0.084261</td>\n",
       "      <td>0.827869</td>\n",
       "      <td>-0.961723</td>\n",
       "      <td>0.757638</td>\n",
       "      <td>1.677346</td>\n",
       "      <td>0.774230</td>\n",
       "      <td>-0.576444</td>\n",
       "      <td>-0.268061</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>-0.953787</td>\n",
       "      <td>-0.810742</td>\n",
       "      <td>-0.569309</td>\n",
       "      <td>-0.466338</td>\n",
       "      <td>-0.064254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2693</th>\n",
       "      <td>-2.221718</td>\n",
       "      <td>0.316398</td>\n",
       "      <td>-0.259833</td>\n",
       "      <td>-1.021322</td>\n",
       "      <td>-0.590147</td>\n",
       "      <td>0.132152</td>\n",
       "      <td>-0.256669</td>\n",
       "      <td>-0.584547</td>\n",
       "      <td>-0.941379</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>0.653021</td>\n",
       "      <td>-0.672255</td>\n",
       "      <td>0.243650</td>\n",
       "      <td>0.372715</td>\n",
       "      <td>-0.634146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>0.247530</td>\n",
       "      <td>0.329096</td>\n",
       "      <td>0.088062</td>\n",
       "      <td>0.929038</td>\n",
       "      <td>0.619683</td>\n",
       "      <td>-0.922191</td>\n",
       "      <td>0.220924</td>\n",
       "      <td>0.345807</td>\n",
       "      <td>1.805030</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.189799</td>\n",
       "      <td>0.118743</td>\n",
       "      <td>1.807917</td>\n",
       "      <td>-0.494322</td>\n",
       "      <td>-0.548433</td>\n",
       "      <td>1.165320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3927</th>\n",
       "      <td>-0.056898</td>\n",
       "      <td>-0.026969</td>\n",
       "      <td>0.697627</td>\n",
       "      <td>1.042327</td>\n",
       "      <td>-1.483930</td>\n",
       "      <td>0.182722</td>\n",
       "      <td>1.301834</td>\n",
       "      <td>0.814760</td>\n",
       "      <td>-0.472847</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>0.653021</td>\n",
       "      <td>0.838510</td>\n",
       "      <td>-0.526218</td>\n",
       "      <td>-0.723133</td>\n",
       "      <td>0.075806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5955</th>\n",
       "      <td>2.006446</td>\n",
       "      <td>-1.978761</td>\n",
       "      <td>1.667188</td>\n",
       "      <td>-0.406979</td>\n",
       "      <td>0.266701</td>\n",
       "      <td>-1.412018</td>\n",
       "      <td>-1.332939</td>\n",
       "      <td>-0.480934</td>\n",
       "      <td>-0.943014</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>-0.685654</td>\n",
       "      <td>0.637074</td>\n",
       "      <td>-0.133199</td>\n",
       "      <td>-0.810993</td>\n",
       "      <td>-1.345214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6936</th>\n",
       "      <td>-0.530452</td>\n",
       "      <td>-1.319506</td>\n",
       "      <td>0.350824</td>\n",
       "      <td>0.064060</td>\n",
       "      <td>-1.408462</td>\n",
       "      <td>0.070027</td>\n",
       "      <td>0.127936</td>\n",
       "      <td>0.273038</td>\n",
       "      <td>-0.653336</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>0.118743</td>\n",
       "      <td>-0.042770</td>\n",
       "      <td>1.294304</td>\n",
       "      <td>-0.484508</td>\n",
       "      <td>0.425472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5640</th>\n",
       "      <td>1.093162</td>\n",
       "      <td>0.050363</td>\n",
       "      <td>0.865340</td>\n",
       "      <td>-0.552903</td>\n",
       "      <td>0.591413</td>\n",
       "      <td>0.330312</td>\n",
       "      <td>-0.490390</td>\n",
       "      <td>0.464323</td>\n",
       "      <td>-0.998551</td>\n",
       "      <td>-0.276020</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>-1.490051</td>\n",
       "      <td>0.045358</td>\n",
       "      <td>0.376283</td>\n",
       "      <td>0.730359</td>\n",
       "      <td>-0.435855</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5426 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Present_Tmax  LDAPS_RHmax  LDAPS_Tmax_lapse  LDAPS_WS  LDAPS_LH  \\\n",
       "593      -1.376085     1.044566         -0.758505  2.397972 -0.319619   \n",
       "6344      0.957861     0.669978          0.853678 -0.051662  1.571078   \n",
       "571       0.315180     0.613097         -0.880037  0.863969 -0.553041   \n",
       "3988      0.349006     0.215510         -0.084261  0.827869 -0.961723   \n",
       "2693     -2.221718     0.316398         -0.259833 -1.021322 -0.590147   \n",
       "...            ...          ...               ...       ...       ...   \n",
       "79        0.247530     0.329096          0.088062  0.929038  0.619683   \n",
       "3927     -0.056898    -0.026969          0.697627  1.042327 -1.483930   \n",
       "5955      2.006446    -1.978761          1.667188 -0.406979  0.266701   \n",
       "6936     -0.530452    -1.319506          0.350824  0.064060 -1.408462   \n",
       "5640      1.093162     0.050363          0.865340 -0.552903  0.591413   \n",
       "\n",
       "      LDAPS_CC1  LDAPS_CC2  LDAPS_CC3  LDAPS_CC4  LDAPS_PPT1  LDAPS_PPT4  \\\n",
       "593    1.403786   0.558225  -0.795155  -0.150445    0.788315   -0.224453   \n",
       "6344  -1.395316  -1.378178  -0.621503   0.355440   -0.305750   -0.215593   \n",
       "571    1.445315   2.078433   0.801649   0.847011   -0.301580   -0.224453   \n",
       "3988   0.757638   1.677346   0.774230  -0.576444   -0.268061   -0.224453   \n",
       "2693   0.132152  -0.256669  -0.584547  -0.941379   -0.305750   -0.224453   \n",
       "...         ...        ...        ...        ...         ...         ...   \n",
       "79    -0.922191   0.220924   0.345807   1.805030   -0.305750   -0.189799   \n",
       "3927   0.182722   1.301834   0.814760  -0.472847   -0.305750   -0.224453   \n",
       "5955  -1.412018  -1.332939  -0.480934  -0.943014   -0.305750   -0.224453   \n",
       "6936   0.070027   0.127936   0.273038  -0.653336   -0.305750   -0.224453   \n",
       "5640   0.330312  -0.490390   0.464323  -0.998551   -0.276020   -0.224453   \n",
       "\n",
       "           lat       lon       DEM     Slope  Solar radiation  \n",
       "593   0.653021 -0.672255  0.243650  0.372715         0.656413  \n",
       "6344  1.457418  0.158666  1.560277  2.534052         1.302602  \n",
       "571  -0.685654  1.191021 -0.735149 -0.820115         0.596108  \n",
       "3988 -0.953787 -0.810742 -0.569309 -0.466338        -0.064254  \n",
       "2693  0.653021 -0.672255  0.243650  0.372715        -0.634146  \n",
       "...        ...       ...       ...       ...              ...  \n",
       "79    0.118743  1.807917 -0.494322 -0.548433         1.165320  \n",
       "3927  0.653021  0.838510 -0.526218 -0.723133         0.075806  \n",
       "5955 -0.685654  0.637074 -0.133199 -0.810993        -1.345214  \n",
       "6936  0.118743 -0.042770  1.294304 -0.484508         0.425472  \n",
       "5640 -1.490051  0.045358  0.376283  0.730359        -0.435855  \n",
       "\n",
       "[5426 rows x 16 columns]"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xtrain"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T01:22:33.969981Z",
     "start_time": "2020-06-16T01:22:33.602723Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 648x432 with 0 Axes>"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f4330e68780>"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f4330ec2f98>]"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Y-TEST')"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Y-PREDICTED')"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#使用散点图观测（10分）\n",
    "simp_size = 200\n",
    "plt.figure(figsize=(9,6))\n",
    "plt.scatter(ytest,y_predict)\n",
    "plt.plot(ytest,ytest,color='black')\n",
    "plt.xlabel(xlabel='Y-TEST')\n",
    "plt.ylabel(ylabel='Y-PREDICTED')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T00:15:25.593168Z",
     "start_time": "2020-06-16T00:15:25.588171Z"
    }
   },
   "source": [
    "'''\n",
    "图中我们可以看到，如果完全拟合，散点应该和直线相重合，这里发现，y_test有一些的异常值，\n",
    "而线性回归模型的一大缺点就是对异常值很敏感，会极大影响模型的准确性，因此，下一步，我们就根据这一点，对模型进行优化\n",
    "'''"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 绘制残差图（10分）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T00:15:26.237802Z",
     "start_time": "2020-06-16T00:15:25.595167Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f4330e3b9b0>"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.LineCollection at 0x7f4330e8df28>"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Y_observed')"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Y_predicted')"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 绘制残差图查看模型的拟合情况（10分）\n",
    "from sklearn.model_selection import cross_val_predict,cross_val_score\n",
    "plt.figure()\n",
    "plt.scatter(ytest,y_predict)\n",
    "plt.hlines(y=0,xmax=3,xmin=-4,colors='black')\n",
    "plt.xlabel('Y_observed')\n",
    "plt.ylabel('Y_predicted')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 绘制相关性热图（10分）\n",
    "\n",
    "**绘制各预测变量间的相关性热图，查看是否存在相关性很高的变量（共线性或近似共线性）**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "#0 1 转码\n",
    "def zero_one(x):\n",
    "    for i in x.columns:\n",
    "        if x[i].dtype == 'object':\n",
    "            dic = dict(zip(list(x[i].value_counts().index),range(x[i].nunique())))\n",
    "            x[i] = x[i].map(dic)\n",
    "    return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T00:15:27.379205Z",
     "start_time": "2020-06-16T00:15:26.240804Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<Figure size 1440x720 with 1 Axes>, <AxesSubplot:>)"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "plt.subplots(figsize=(20,10))\n",
    "sns.heatmap(Max.corr(),vmin=0,\n",
    "    vmax=1,\n",
    "    cmap=None,annot=True,\n",
    "    center=0)\n",
    "\n",
    "plt.rcParams['font.size']=15"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 模型调优"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 删除异常值（10分）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T01:35:34.870734Z",
     "start_time": "2020-06-16T01:35:34.674966Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#####================寻找异常值====================######\n",
    "\n",
    "Scaled_Data['Next_Tmax'].plot.box() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T02:28:18.424085Z",
     "start_time": "2020-06-16T02:28:18.411090Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 删除掉Next_Tmax中小于下边缘值的记录（10分）\n",
    "Q1 = Scaled_Data['Next_Tmax'].quantile(0.25) # 下四分位\n",
    "Q3 = Scaled_Data['Next_Tmax'].quantile(0.75)# 上四分位\n",
    "IQR = Q3-Q1\n",
    "low_whisker = Q1-1.5*IQR # 下边缘值\n",
    "\n",
    "\n",
    "Scaled_Data_n  = Scaled_Data[Scaled_Data['Next_Tmax'] > bottom_edge]\n",
    "Scaled_Data_n['Next_Tmax'].plot.box()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T02:16:00.616309Z",
     "start_time": "2020-06-16T02:16:00.579332Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Present_Tmax</th>\n",
       "      <th>LDAPS_RHmax</th>\n",
       "      <th>LDAPS_Tmax_lapse</th>\n",
       "      <th>LDAPS_WS</th>\n",
       "      <th>LDAPS_LH</th>\n",
       "      <th>LDAPS_CC1</th>\n",
       "      <th>LDAPS_CC2</th>\n",
       "      <th>LDAPS_CC3</th>\n",
       "      <th>LDAPS_CC4</th>\n",
       "      <th>LDAPS_PPT1</th>\n",
       "      <th>LDAPS_PPT4</th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>DEM</th>\n",
       "      <th>Slope</th>\n",
       "      <th>Solar radiation</th>\n",
       "      <th>Next_Tmax</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2650</th>\n",
       "      <td>-2.086416</td>\n",
       "      <td>1.146897</td>\n",
       "      <td>-2.217884</td>\n",
       "      <td>-1.247235</td>\n",
       "      <td>-0.404519</td>\n",
       "      <td>1.025171</td>\n",
       "      <td>1.294520</td>\n",
       "      <td>0.671912</td>\n",
       "      <td>0.915515</td>\n",
       "      <td>-0.086042</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>1.189286</td>\n",
       "      <td>-0.005000</td>\n",
       "      <td>2.772243</td>\n",
       "      <td>1.115004</td>\n",
       "      <td>-0.295389</td>\n",
       "      <td>-2.938450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2750</th>\n",
       "      <td>-2.086416</td>\n",
       "      <td>0.722336</td>\n",
       "      <td>-2.347124</td>\n",
       "      <td>0.720893</td>\n",
       "      <td>0.431804</td>\n",
       "      <td>0.990499</td>\n",
       "      <td>1.166849</td>\n",
       "      <td>1.394751</td>\n",
       "      <td>2.292510</td>\n",
       "      <td>-0.188155</td>\n",
       "      <td>3.329386</td>\n",
       "      <td>1.189286</td>\n",
       "      <td>-0.005000</td>\n",
       "      <td>2.772243</td>\n",
       "      <td>1.115004</td>\n",
       "      <td>-0.592600</td>\n",
       "      <td>-3.002504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5025</th>\n",
       "      <td>-1.443736</td>\n",
       "      <td>1.297482</td>\n",
       "      <td>-2.698896</td>\n",
       "      <td>3.154805</td>\n",
       "      <td>0.682660</td>\n",
       "      <td>1.256502</td>\n",
       "      <td>1.666250</td>\n",
       "      <td>1.293021</td>\n",
       "      <td>0.393944</td>\n",
       "      <td>-0.067187</td>\n",
       "      <td>0.123279</td>\n",
       "      <td>1.189286</td>\n",
       "      <td>-0.005000</td>\n",
       "      <td>2.772243</td>\n",
       "      <td>1.115004</td>\n",
       "      <td>1.165416</td>\n",
       "      <td>-3.066558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5026</th>\n",
       "      <td>-0.564278</td>\n",
       "      <td>1.141629</td>\n",
       "      <td>-2.189783</td>\n",
       "      <td>0.265648</td>\n",
       "      <td>-0.153150</td>\n",
       "      <td>1.590055</td>\n",
       "      <td>1.613945</td>\n",
       "      <td>1.371150</td>\n",
       "      <td>0.589398</td>\n",
       "      <td>-0.085760</td>\n",
       "      <td>0.215901</td>\n",
       "      <td>1.189286</td>\n",
       "      <td>0.511177</td>\n",
       "      <td>-0.315157</td>\n",
       "      <td>-0.542158</td>\n",
       "      <td>0.865895</td>\n",
       "      <td>-2.810342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5050</th>\n",
       "      <td>-3.067350</td>\n",
       "      <td>1.585246</td>\n",
       "      <td>-2.476358</td>\n",
       "      <td>1.396136</td>\n",
       "      <td>-0.782847</td>\n",
       "      <td>0.501491</td>\n",
       "      <td>-0.052399</td>\n",
       "      <td>-0.043444</td>\n",
       "      <td>0.101308</td>\n",
       "      <td>0.072443</td>\n",
       "      <td>-0.138037</td>\n",
       "      <td>1.189286</td>\n",
       "      <td>-0.005000</td>\n",
       "      <td>2.772243</td>\n",
       "      <td>1.115004</td>\n",
       "      <td>1.131952</td>\n",
       "      <td>-3.066558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6100</th>\n",
       "      <td>-1.646687</td>\n",
       "      <td>0.457765</td>\n",
       "      <td>-2.310616</td>\n",
       "      <td>1.833989</td>\n",
       "      <td>1.489480</td>\n",
       "      <td>1.192236</td>\n",
       "      <td>0.211053</td>\n",
       "      <td>0.341799</td>\n",
       "      <td>1.351480</td>\n",
       "      <td>-0.204227</td>\n",
       "      <td>0.056343</td>\n",
       "      <td>1.189286</td>\n",
       "      <td>-0.005000</td>\n",
       "      <td>2.772243</td>\n",
       "      <td>1.115004</td>\n",
       "      <td>-1.511327</td>\n",
       "      <td>-3.034531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6175</th>\n",
       "      <td>-2.695272</td>\n",
       "      <td>1.457979</td>\n",
       "      <td>-4.087857</td>\n",
       "      <td>4.911091</td>\n",
       "      <td>-0.210420</td>\n",
       "      <td>1.489658</td>\n",
       "      <td>2.281600</td>\n",
       "      <td>2.656311</td>\n",
       "      <td>2.268567</td>\n",
       "      <td>-0.017489</td>\n",
       "      <td>1.340116</td>\n",
       "      <td>1.189286</td>\n",
       "      <td>-0.005000</td>\n",
       "      <td>2.772243</td>\n",
       "      <td>1.115004</td>\n",
       "      <td>-1.786106</td>\n",
       "      <td>-4.123453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6176</th>\n",
       "      <td>-2.086416</td>\n",
       "      <td>0.517310</td>\n",
       "      <td>-3.535766</td>\n",
       "      <td>4.003889</td>\n",
       "      <td>1.463660</td>\n",
       "      <td>0.894764</td>\n",
       "      <td>1.277177</td>\n",
       "      <td>2.148085</td>\n",
       "      <td>1.600057</td>\n",
       "      <td>0.084708</td>\n",
       "      <td>2.187907</td>\n",
       "      <td>1.189286</td>\n",
       "      <td>0.511177</td>\n",
       "      <td>-0.315157</td>\n",
       "      <td>-0.542158</td>\n",
       "      <td>-2.138584</td>\n",
       "      <td>-3.258721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6177</th>\n",
       "      <td>-1.815814</td>\n",
       "      <td>0.293287</td>\n",
       "      <td>-3.323528</td>\n",
       "      <td>3.380546</td>\n",
       "      <td>0.732305</td>\n",
       "      <td>1.483952</td>\n",
       "      <td>1.747604</td>\n",
       "      <td>2.166054</td>\n",
       "      <td>1.581048</td>\n",
       "      <td>-0.091321</td>\n",
       "      <td>1.378723</td>\n",
       "      <td>0.653021</td>\n",
       "      <td>0.838510</td>\n",
       "      <td>-0.526218</td>\n",
       "      <td>-0.723133</td>\n",
       "      <td>-2.150110</td>\n",
       "      <td>-3.194667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6178</th>\n",
       "      <td>-1.815814</td>\n",
       "      <td>1.381461</td>\n",
       "      <td>-3.854778</td>\n",
       "      <td>5.508391</td>\n",
       "      <td>1.053051</td>\n",
       "      <td>1.129946</td>\n",
       "      <td>2.138966</td>\n",
       "      <td>2.557734</td>\n",
       "      <td>1.943433</td>\n",
       "      <td>0.196184</td>\n",
       "      <td>1.718829</td>\n",
       "      <td>1.991696</td>\n",
       "      <td>0.385280</td>\n",
       "      <td>-0.297588</td>\n",
       "      <td>0.932424</td>\n",
       "      <td>-2.174068</td>\n",
       "      <td>-3.098586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6179</th>\n",
       "      <td>-1.748163</td>\n",
       "      <td>0.540625</td>\n",
       "      <td>-3.180434</td>\n",
       "      <td>2.709573</td>\n",
       "      <td>0.667897</td>\n",
       "      <td>1.427806</td>\n",
       "      <td>2.207221</td>\n",
       "      <td>2.466783</td>\n",
       "      <td>1.705215</td>\n",
       "      <td>-0.208367</td>\n",
       "      <td>1.190854</td>\n",
       "      <td>0.118743</td>\n",
       "      <td>1.807917</td>\n",
       "      <td>-0.494322</td>\n",
       "      <td>-0.548433</td>\n",
       "      <td>-2.181193</td>\n",
       "      <td>-3.194667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6180</th>\n",
       "      <td>-1.849639</td>\n",
       "      <td>0.291101</td>\n",
       "      <td>-3.008869</td>\n",
       "      <td>3.443116</td>\n",
       "      <td>0.875766</td>\n",
       "      <td>1.527562</td>\n",
       "      <td>2.186085</td>\n",
       "      <td>2.380435</td>\n",
       "      <td>1.732806</td>\n",
       "      <td>-0.111499</td>\n",
       "      <td>0.530213</td>\n",
       "      <td>-0.685654</td>\n",
       "      <td>0.637074</td>\n",
       "      <td>-0.133199</td>\n",
       "      <td>-0.810993</td>\n",
       "      <td>-2.150112</td>\n",
       "      <td>-2.906423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6181</th>\n",
       "      <td>-2.086416</td>\n",
       "      <td>0.726924</td>\n",
       "      <td>-3.416935</td>\n",
       "      <td>3.153251</td>\n",
       "      <td>1.033799</td>\n",
       "      <td>1.484045</td>\n",
       "      <td>2.193447</td>\n",
       "      <td>2.587556</td>\n",
       "      <td>2.013460</td>\n",
       "      <td>-0.123314</td>\n",
       "      <td>0.618552</td>\n",
       "      <td>0.653021</td>\n",
       "      <td>-1.931225</td>\n",
       "      <td>-0.911963</td>\n",
       "      <td>-0.845437</td>\n",
       "      <td>-2.193176</td>\n",
       "      <td>-3.034531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6182</th>\n",
       "      <td>-1.849639</td>\n",
       "      <td>0.128079</td>\n",
       "      <td>-2.987955</td>\n",
       "      <td>3.182614</td>\n",
       "      <td>0.539878</td>\n",
       "      <td>1.737777</td>\n",
       "      <td>2.324832</td>\n",
       "      <td>2.536665</td>\n",
       "      <td>1.386484</td>\n",
       "      <td>0.099575</td>\n",
       "      <td>0.452257</td>\n",
       "      <td>-1.490051</td>\n",
       "      <td>-1.024766</td>\n",
       "      <td>-0.172266</td>\n",
       "      <td>0.223191</td>\n",
       "      <td>-2.197299</td>\n",
       "      <td>-2.970477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6183</th>\n",
       "      <td>-1.883465</td>\n",
       "      <td>0.559954</td>\n",
       "      <td>-3.090460</td>\n",
       "      <td>3.785644</td>\n",
       "      <td>0.588896</td>\n",
       "      <td>1.794153</td>\n",
       "      <td>2.241939</td>\n",
       "      <td>2.621514</td>\n",
       "      <td>1.616357</td>\n",
       "      <td>0.058270</td>\n",
       "      <td>0.359513</td>\n",
       "      <td>-0.953787</td>\n",
       "      <td>-2.082302</td>\n",
       "      <td>-0.201502</td>\n",
       "      <td>-0.616299</td>\n",
       "      <td>-2.121602</td>\n",
       "      <td>-3.066558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6184</th>\n",
       "      <td>-2.390844</td>\n",
       "      <td>0.548491</td>\n",
       "      <td>-3.356360</td>\n",
       "      <td>3.778144</td>\n",
       "      <td>0.340973</td>\n",
       "      <td>1.722154</td>\n",
       "      <td>2.379023</td>\n",
       "      <td>2.612524</td>\n",
       "      <td>1.583916</td>\n",
       "      <td>0.143382</td>\n",
       "      <td>0.585596</td>\n",
       "      <td>-1.758184</td>\n",
       "      <td>-0.458230</td>\n",
       "      <td>2.701715</td>\n",
       "      <td>2.861413</td>\n",
       "      <td>-2.358212</td>\n",
       "      <td>-3.194667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6185</th>\n",
       "      <td>-1.748163</td>\n",
       "      <td>0.073370</td>\n",
       "      <td>-2.977437</td>\n",
       "      <td>2.994312</td>\n",
       "      <td>0.760935</td>\n",
       "      <td>1.561845</td>\n",
       "      <td>2.072250</td>\n",
       "      <td>2.185478</td>\n",
       "      <td>1.552148</td>\n",
       "      <td>-0.177150</td>\n",
       "      <td>0.826247</td>\n",
       "      <td>-0.149390</td>\n",
       "      <td>1.178432</td>\n",
       "      <td>-0.611095</td>\n",
       "      <td>-0.462470</td>\n",
       "      <td>-2.131853</td>\n",
       "      <td>-3.066558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6186</th>\n",
       "      <td>-2.357019</td>\n",
       "      <td>0.397078</td>\n",
       "      <td>-3.309947</td>\n",
       "      <td>3.641401</td>\n",
       "      <td>0.469220</td>\n",
       "      <td>1.452282</td>\n",
       "      <td>2.096269</td>\n",
       "      <td>2.496450</td>\n",
       "      <td>1.974161</td>\n",
       "      <td>-0.132036</td>\n",
       "      <td>0.559798</td>\n",
       "      <td>0.118743</td>\n",
       "      <td>-0.042770</td>\n",
       "      <td>1.294304</td>\n",
       "      <td>-0.484508</td>\n",
       "      <td>-2.046890</td>\n",
       "      <td>-3.739128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6187</th>\n",
       "      <td>-1.815814</td>\n",
       "      <td>0.203483</td>\n",
       "      <td>-3.346815</td>\n",
       "      <td>2.572588</td>\n",
       "      <td>0.430980</td>\n",
       "      <td>1.597681</td>\n",
       "      <td>1.913452</td>\n",
       "      <td>2.183086</td>\n",
       "      <td>1.684987</td>\n",
       "      <td>-0.163895</td>\n",
       "      <td>1.556727</td>\n",
       "      <td>0.653021</td>\n",
       "      <td>1.153252</td>\n",
       "      <td>-0.037504</td>\n",
       "      <td>1.043125</td>\n",
       "      <td>-2.208229</td>\n",
       "      <td>-3.130613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6188</th>\n",
       "      <td>-1.917290</td>\n",
       "      <td>0.196464</td>\n",
       "      <td>-2.927473</td>\n",
       "      <td>3.375313</td>\n",
       "      <td>0.684771</td>\n",
       "      <td>1.560831</td>\n",
       "      <td>2.231691</td>\n",
       "      <td>2.590861</td>\n",
       "      <td>1.712657</td>\n",
       "      <td>-0.069786</td>\n",
       "      <td>0.474994</td>\n",
       "      <td>-0.953787</td>\n",
       "      <td>-0.810742</td>\n",
       "      <td>-0.569309</td>\n",
       "      <td>-0.466338</td>\n",
       "      <td>-2.181012</td>\n",
       "      <td>-2.938450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6189</th>\n",
       "      <td>-1.748163</td>\n",
       "      <td>0.335249</td>\n",
       "      <td>-3.121586</td>\n",
       "      <td>3.246297</td>\n",
       "      <td>0.445623</td>\n",
       "      <td>1.492428</td>\n",
       "      <td>2.204697</td>\n",
       "      <td>2.533502</td>\n",
       "      <td>1.996110</td>\n",
       "      <td>-0.150345</td>\n",
       "      <td>0.469066</td>\n",
       "      <td>0.118743</td>\n",
       "      <td>-0.684844</td>\n",
       "      <td>-0.586289</td>\n",
       "      <td>-0.293244</td>\n",
       "      <td>-2.122811</td>\n",
       "      <td>-3.162640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6190</th>\n",
       "      <td>-2.052591</td>\n",
       "      <td>0.342238</td>\n",
       "      <td>-3.133392</td>\n",
       "      <td>3.608837</td>\n",
       "      <td>0.689004</td>\n",
       "      <td>1.649470</td>\n",
       "      <td>2.317912</td>\n",
       "      <td>2.603041</td>\n",
       "      <td>1.430013</td>\n",
       "      <td>-0.014496</td>\n",
       "      <td>0.546835</td>\n",
       "      <td>-1.490051</td>\n",
       "      <td>0.045358</td>\n",
       "      <td>0.376283</td>\n",
       "      <td>0.730359</td>\n",
       "      <td>-2.274529</td>\n",
       "      <td>-3.066558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6191</th>\n",
       "      <td>-2.018766</td>\n",
       "      <td>0.443371</td>\n",
       "      <td>-3.559795</td>\n",
       "      <td>2.056200</td>\n",
       "      <td>0.187604</td>\n",
       "      <td>1.580071</td>\n",
       "      <td>2.165352</td>\n",
       "      <td>2.341953</td>\n",
       "      <td>2.004631</td>\n",
       "      <td>-0.191814</td>\n",
       "      <td>2.098272</td>\n",
       "      <td>1.457418</td>\n",
       "      <td>1.354688</td>\n",
       "      <td>-0.154704</td>\n",
       "      <td>-0.408689</td>\n",
       "      <td>-2.146457</td>\n",
       "      <td>-3.386829</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6193</th>\n",
       "      <td>-1.781989</td>\n",
       "      <td>0.683279</td>\n",
       "      <td>-3.546341</td>\n",
       "      <td>3.042051</td>\n",
       "      <td>0.351990</td>\n",
       "      <td>1.475464</td>\n",
       "      <td>2.204002</td>\n",
       "      <td>2.544886</td>\n",
       "      <td>2.064549</td>\n",
       "      <td>-0.164838</td>\n",
       "      <td>0.821398</td>\n",
       "      <td>0.653021</td>\n",
       "      <td>-0.672255</td>\n",
       "      <td>0.243650</td>\n",
       "      <td>0.372715</td>\n",
       "      <td>-2.064552</td>\n",
       "      <td>-3.643046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6194</th>\n",
       "      <td>-1.917290</td>\n",
       "      <td>1.544363</td>\n",
       "      <td>-4.016045</td>\n",
       "      <td>5.989302</td>\n",
       "      <td>-0.963308</td>\n",
       "      <td>1.394206</td>\n",
       "      <td>2.330022</td>\n",
       "      <td>2.670813</td>\n",
       "      <td>2.371793</td>\n",
       "      <td>0.122695</td>\n",
       "      <td>1.598140</td>\n",
       "      <td>1.457418</td>\n",
       "      <td>0.158666</td>\n",
       "      <td>1.560277</td>\n",
       "      <td>2.534052</td>\n",
       "      <td>-1.925129</td>\n",
       "      <td>-3.450884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6195</th>\n",
       "      <td>-1.984940</td>\n",
       "      <td>0.181415</td>\n",
       "      <td>-3.033073</td>\n",
       "      <td>3.594376</td>\n",
       "      <td>0.729578</td>\n",
       "      <td>1.494870</td>\n",
       "      <td>1.970933</td>\n",
       "      <td>2.330075</td>\n",
       "      <td>1.676683</td>\n",
       "      <td>-0.125777</td>\n",
       "      <td>0.724950</td>\n",
       "      <td>0.118743</td>\n",
       "      <td>0.611895</td>\n",
       "      <td>-0.655350</td>\n",
       "      <td>-0.499833</td>\n",
       "      <td>-2.156557</td>\n",
       "      <td>-2.970477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6196</th>\n",
       "      <td>-1.815814</td>\n",
       "      <td>0.273083</td>\n",
       "      <td>-2.951532</td>\n",
       "      <td>3.104686</td>\n",
       "      <td>0.981366</td>\n",
       "      <td>1.602459</td>\n",
       "      <td>2.125604</td>\n",
       "      <td>2.416907</td>\n",
       "      <td>1.457133</td>\n",
       "      <td>-0.151949</td>\n",
       "      <td>0.611140</td>\n",
       "      <td>-0.685654</td>\n",
       "      <td>1.191021</td>\n",
       "      <td>-0.735149</td>\n",
       "      <td>-0.820115</td>\n",
       "      <td>-2.190000</td>\n",
       "      <td>-2.842369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6197</th>\n",
       "      <td>-1.680513</td>\n",
       "      <td>0.353661</td>\n",
       "      <td>-3.003557</td>\n",
       "      <td>3.407620</td>\n",
       "      <td>0.675525</td>\n",
       "      <td>1.534201</td>\n",
       "      <td>2.312147</td>\n",
       "      <td>2.602183</td>\n",
       "      <td>2.024745</td>\n",
       "      <td>-0.092121</td>\n",
       "      <td>0.400733</td>\n",
       "      <td>-0.149390</td>\n",
       "      <td>-1.263971</td>\n",
       "      <td>-0.852681</td>\n",
       "      <td>-0.803915</td>\n",
       "      <td>-2.186347</td>\n",
       "      <td>-2.906423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6198</th>\n",
       "      <td>-1.680513</td>\n",
       "      <td>0.220089</td>\n",
       "      <td>-2.929177</td>\n",
       "      <td>3.410922</td>\n",
       "      <td>0.694940</td>\n",
       "      <td>1.498068</td>\n",
       "      <td>2.302101</td>\n",
       "      <td>2.556029</td>\n",
       "      <td>1.916674</td>\n",
       "      <td>-0.097121</td>\n",
       "      <td>0.454645</td>\n",
       "      <td>-0.417522</td>\n",
       "      <td>-1.037356</td>\n",
       "      <td>-0.821213</td>\n",
       "      <td>-0.755095</td>\n",
       "      <td>-2.198020</td>\n",
       "      <td>-2.938450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6199</th>\n",
       "      <td>-1.883465</td>\n",
       "      <td>0.225614</td>\n",
       "      <td>-2.937813</td>\n",
       "      <td>3.387032</td>\n",
       "      <td>0.635237</td>\n",
       "      <td>1.499937</td>\n",
       "      <td>2.102797</td>\n",
       "      <td>2.516695</td>\n",
       "      <td>1.838733</td>\n",
       "      <td>-0.120700</td>\n",
       "      <td>0.500970</td>\n",
       "      <td>-0.417522</td>\n",
       "      <td>-0.269384</td>\n",
       "      <td>-0.779043</td>\n",
       "      <td>-0.719338</td>\n",
       "      <td>-2.167465</td>\n",
       "      <td>-2.938450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7675</th>\n",
       "      <td>-2.221718</td>\n",
       "      <td>0.061175</td>\n",
       "      <td>-2.850518</td>\n",
       "      <td>3.165786</td>\n",
       "      <td>2.037223</td>\n",
       "      <td>0.014291</td>\n",
       "      <td>-1.059738</td>\n",
       "      <td>-1.063356</td>\n",
       "      <td>-0.784703</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>1.189286</td>\n",
       "      <td>-0.005000</td>\n",
       "      <td>2.772243</td>\n",
       "      <td>1.115004</td>\n",
       "      <td>-1.511327</td>\n",
       "      <td>-3.034531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7700</th>\n",
       "      <td>-3.202651</td>\n",
       "      <td>-0.069554</td>\n",
       "      <td>-3.181468</td>\n",
       "      <td>-0.296906</td>\n",
       "      <td>0.184863</td>\n",
       "      <td>0.946993</td>\n",
       "      <td>1.139885</td>\n",
       "      <td>0.571433</td>\n",
       "      <td>-0.817978</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>1.189286</td>\n",
       "      <td>-0.005000</td>\n",
       "      <td>2.772243</td>\n",
       "      <td>1.115004</td>\n",
       "      <td>-1.601879</td>\n",
       "      <td>-3.162640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7750</th>\n",
       "      <td>-3.304127</td>\n",
       "      <td>-4.113443</td>\n",
       "      <td>-4.087857</td>\n",
       "      <td>-1.939757</td>\n",
       "      <td>-2.267499</td>\n",
       "      <td>-1.412018</td>\n",
       "      <td>-1.386643</td>\n",
       "      <td>-1.278054</td>\n",
       "      <td>-1.182118</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>-1.758184</td>\n",
       "      <td>-2.082302</td>\n",
       "      <td>-0.911963</td>\n",
       "      <td>-0.845455</td>\n",
       "      <td>-2.358212</td>\n",
       "      <td>-4.123453</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Present_Tmax  LDAPS_RHmax  LDAPS_Tmax_lapse  LDAPS_WS  LDAPS_LH  \\\n",
       "2650     -2.086416     1.146897         -2.217884 -1.247235 -0.404519   \n",
       "2750     -2.086416     0.722336         -2.347124  0.720893  0.431804   \n",
       "5025     -1.443736     1.297482         -2.698896  3.154805  0.682660   \n",
       "5026     -0.564278     1.141629         -2.189783  0.265648 -0.153150   \n",
       "5050     -3.067350     1.585246         -2.476358  1.396136 -0.782847   \n",
       "6100     -1.646687     0.457765         -2.310616  1.833989  1.489480   \n",
       "6175     -2.695272     1.457979         -4.087857  4.911091 -0.210420   \n",
       "6176     -2.086416     0.517310         -3.535766  4.003889  1.463660   \n",
       "6177     -1.815814     0.293287         -3.323528  3.380546  0.732305   \n",
       "6178     -1.815814     1.381461         -3.854778  5.508391  1.053051   \n",
       "6179     -1.748163     0.540625         -3.180434  2.709573  0.667897   \n",
       "6180     -1.849639     0.291101         -3.008869  3.443116  0.875766   \n",
       "6181     -2.086416     0.726924         -3.416935  3.153251  1.033799   \n",
       "6182     -1.849639     0.128079         -2.987955  3.182614  0.539878   \n",
       "6183     -1.883465     0.559954         -3.090460  3.785644  0.588896   \n",
       "6184     -2.390844     0.548491         -3.356360  3.778144  0.340973   \n",
       "6185     -1.748163     0.073370         -2.977437  2.994312  0.760935   \n",
       "6186     -2.357019     0.397078         -3.309947  3.641401  0.469220   \n",
       "6187     -1.815814     0.203483         -3.346815  2.572588  0.430980   \n",
       "6188     -1.917290     0.196464         -2.927473  3.375313  0.684771   \n",
       "6189     -1.748163     0.335249         -3.121586  3.246297  0.445623   \n",
       "6190     -2.052591     0.342238         -3.133392  3.608837  0.689004   \n",
       "6191     -2.018766     0.443371         -3.559795  2.056200  0.187604   \n",
       "6193     -1.781989     0.683279         -3.546341  3.042051  0.351990   \n",
       "6194     -1.917290     1.544363         -4.016045  5.989302 -0.963308   \n",
       "6195     -1.984940     0.181415         -3.033073  3.594376  0.729578   \n",
       "6196     -1.815814     0.273083         -2.951532  3.104686  0.981366   \n",
       "6197     -1.680513     0.353661         -3.003557  3.407620  0.675525   \n",
       "6198     -1.680513     0.220089         -2.929177  3.410922  0.694940   \n",
       "6199     -1.883465     0.225614         -2.937813  3.387032  0.635237   \n",
       "7675     -2.221718     0.061175         -2.850518  3.165786  2.037223   \n",
       "7700     -3.202651    -0.069554         -3.181468 -0.296906  0.184863   \n",
       "7750     -3.304127    -4.113443         -4.087857 -1.939757 -2.267499   \n",
       "\n",
       "      LDAPS_CC1  LDAPS_CC2  LDAPS_CC3  LDAPS_CC4  LDAPS_PPT1  LDAPS_PPT4  \\\n",
       "2650   1.025171   1.294520   0.671912   0.915515   -0.086042   -0.224453   \n",
       "2750   0.990499   1.166849   1.394751   2.292510   -0.188155    3.329386   \n",
       "5025   1.256502   1.666250   1.293021   0.393944   -0.067187    0.123279   \n",
       "5026   1.590055   1.613945   1.371150   0.589398   -0.085760    0.215901   \n",
       "5050   0.501491  -0.052399  -0.043444   0.101308    0.072443   -0.138037   \n",
       "6100   1.192236   0.211053   0.341799   1.351480   -0.204227    0.056343   \n",
       "6175   1.489658   2.281600   2.656311   2.268567   -0.017489    1.340116   \n",
       "6176   0.894764   1.277177   2.148085   1.600057    0.084708    2.187907   \n",
       "6177   1.483952   1.747604   2.166054   1.581048   -0.091321    1.378723   \n",
       "6178   1.129946   2.138966   2.557734   1.943433    0.196184    1.718829   \n",
       "6179   1.427806   2.207221   2.466783   1.705215   -0.208367    1.190854   \n",
       "6180   1.527562   2.186085   2.380435   1.732806   -0.111499    0.530213   \n",
       "6181   1.484045   2.193447   2.587556   2.013460   -0.123314    0.618552   \n",
       "6182   1.737777   2.324832   2.536665   1.386484    0.099575    0.452257   \n",
       "6183   1.794153   2.241939   2.621514   1.616357    0.058270    0.359513   \n",
       "6184   1.722154   2.379023   2.612524   1.583916    0.143382    0.585596   \n",
       "6185   1.561845   2.072250   2.185478   1.552148   -0.177150    0.826247   \n",
       "6186   1.452282   2.096269   2.496450   1.974161   -0.132036    0.559798   \n",
       "6187   1.597681   1.913452   2.183086   1.684987   -0.163895    1.556727   \n",
       "6188   1.560831   2.231691   2.590861   1.712657   -0.069786    0.474994   \n",
       "6189   1.492428   2.204697   2.533502   1.996110   -0.150345    0.469066   \n",
       "6190   1.649470   2.317912   2.603041   1.430013   -0.014496    0.546835   \n",
       "6191   1.580071   2.165352   2.341953   2.004631   -0.191814    2.098272   \n",
       "6193   1.475464   2.204002   2.544886   2.064549   -0.164838    0.821398   \n",
       "6194   1.394206   2.330022   2.670813   2.371793    0.122695    1.598140   \n",
       "6195   1.494870   1.970933   2.330075   1.676683   -0.125777    0.724950   \n",
       "6196   1.602459   2.125604   2.416907   1.457133   -0.151949    0.611140   \n",
       "6197   1.534201   2.312147   2.602183   2.024745   -0.092121    0.400733   \n",
       "6198   1.498068   2.302101   2.556029   1.916674   -0.097121    0.454645   \n",
       "6199   1.499937   2.102797   2.516695   1.838733   -0.120700    0.500970   \n",
       "7675   0.014291  -1.059738  -1.063356  -0.784703   -0.305750   -0.224453   \n",
       "7700   0.946993   1.139885   0.571433  -0.817978   -0.305750   -0.224453   \n",
       "7750  -1.412018  -1.386643  -1.278054  -1.182118   -0.305750   -0.224453   \n",
       "\n",
       "           lat       lon       DEM     Slope  Solar radiation  Next_Tmax  \n",
       "2650  1.189286 -0.005000  2.772243  1.115004        -0.295389  -2.938450  \n",
       "2750  1.189286 -0.005000  2.772243  1.115004        -0.592600  -3.002504  \n",
       "5025  1.189286 -0.005000  2.772243  1.115004         1.165416  -3.066558  \n",
       "5026  1.189286  0.511177 -0.315157 -0.542158         0.865895  -2.810342  \n",
       "5050  1.189286 -0.005000  2.772243  1.115004         1.131952  -3.066558  \n",
       "6100  1.189286 -0.005000  2.772243  1.115004        -1.511327  -3.034531  \n",
       "6175  1.189286 -0.005000  2.772243  1.115004        -1.786106  -4.123453  \n",
       "6176  1.189286  0.511177 -0.315157 -0.542158        -2.138584  -3.258721  \n",
       "6177  0.653021  0.838510 -0.526218 -0.723133        -2.150110  -3.194667  \n",
       "6178  1.991696  0.385280 -0.297588  0.932424        -2.174068  -3.098586  \n",
       "6179  0.118743  1.807917 -0.494322 -0.548433        -2.181193  -3.194667  \n",
       "6180 -0.685654  0.637074 -0.133199 -0.810993        -2.150112  -2.906423  \n",
       "6181  0.653021 -1.931225 -0.911963 -0.845437        -2.193176  -3.034531  \n",
       "6182 -1.490051 -1.024766 -0.172266  0.223191        -2.197299  -2.970477  \n",
       "6183 -0.953787 -2.082302 -0.201502 -0.616299        -2.121602  -3.066558  \n",
       "6184 -1.758184 -0.458230  2.701715  2.861413        -2.358212  -3.194667  \n",
       "6185 -0.149390  1.178432 -0.611095 -0.462470        -2.131853  -3.066558  \n",
       "6186  0.118743 -0.042770  1.294304 -0.484508        -2.046890  -3.739128  \n",
       "6187  0.653021  1.153252 -0.037504  1.043125        -2.208229  -3.130613  \n",
       "6188 -0.953787 -0.810742 -0.569309 -0.466338        -2.181012  -2.938450  \n",
       "6189  0.118743 -0.684844 -0.586289 -0.293244        -2.122811  -3.162640  \n",
       "6190 -1.490051  0.045358  0.376283  0.730359        -2.274529  -3.066558  \n",
       "6191  1.457418  1.354688 -0.154704 -0.408689        -2.146457  -3.386829  \n",
       "6193  0.653021 -0.672255  0.243650  0.372715        -2.064552  -3.643046  \n",
       "6194  1.457418  0.158666  1.560277  2.534052        -1.925129  -3.450884  \n",
       "6195  0.118743  0.611895 -0.655350 -0.499833        -2.156557  -2.970477  \n",
       "6196 -0.685654  1.191021 -0.735149 -0.820115        -2.190000  -2.842369  \n",
       "6197 -0.149390 -1.263971 -0.852681 -0.803915        -2.186347  -2.906423  \n",
       "6198 -0.417522 -1.037356 -0.821213 -0.755095        -2.198020  -2.938450  \n",
       "6199 -0.417522 -0.269384 -0.779043 -0.719338        -2.167465  -2.938450  \n",
       "7675  1.189286 -0.005000  2.772243  1.115004        -1.511327  -3.034531  \n",
       "7700  1.189286 -0.005000  2.772243  1.115004        -1.601879  -3.162640  \n",
       "7750 -1.758184 -2.082302 -0.911963 -0.845455        -2.358212  -4.123453  "
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Y中小于箱型图下边缘的值\n",
    "Scaled_Data[Scaled_Data['Next_Tmax']<low_whisker] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T02:24:40.455480Z",
     "start_time": "2020-06-16T02:24:40.448484Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2650, 2750, 5025, 5026, 5050, 6100, 6175, 6176, 6177, 6178, 6179,\n",
       "       6180, 6181, 6182, 6183, 6184, 6185, 6186, 6187, 6188, 6189, 6190,\n",
       "       6191, 6193, 6194, 6195, 6196, 6197, 6198, 6199, 7675, 7700, 7750])"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 要删除的样本的Index\n",
    "drop_index=Scaled_Data[Scaled_Data['Next_Tmax']<low_whisker].index.values\n",
    "drop_index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T02:25:42.036217Z",
     "start_time": "2020-06-16T02:25:41.961262Z"
    }
   },
   "outputs": [],
   "source": [
    "# 删除Y中小于箱型图下边缘的值\n",
    "X=X.drop(drop_index)\n",
    "Y=Y.drop(drop_index) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T02:25:54.069979Z",
     "start_time": "2020-06-16T02:25:54.035982Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Present_Tmax</th>\n",
       "      <th>LDAPS_RHmax</th>\n",
       "      <th>LDAPS_Tmax_lapse</th>\n",
       "      <th>LDAPS_WS</th>\n",
       "      <th>LDAPS_LH</th>\n",
       "      <th>LDAPS_CC1</th>\n",
       "      <th>LDAPS_CC2</th>\n",
       "      <th>LDAPS_CC3</th>\n",
       "      <th>LDAPS_CC4</th>\n",
       "      <th>LDAPS_PPT1</th>\n",
       "      <th>LDAPS_PPT4</th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>DEM</th>\n",
       "      <th>Slope</th>\n",
       "      <th>Solar radiation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.361326</td>\n",
       "      <td>0.383078</td>\n",
       "      <td>-0.524889</td>\n",
       "      <td>-0.128382</td>\n",
       "      <td>0.206966</td>\n",
       "      <td>-0.516243</td>\n",
       "      <td>-0.592636</td>\n",
       "      <td>-0.629013</td>\n",
       "      <td>-0.664815</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>1.189286</td>\n",
       "      <td>-0.005000</td>\n",
       "      <td>2.772243</td>\n",
       "      <td>1.115004</td>\n",
       "      <td>1.517935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.721084</td>\n",
       "      <td>0.311586</td>\n",
       "      <td>0.080895</td>\n",
       "      <td>-0.646994</td>\n",
       "      <td>-0.314841</td>\n",
       "      <td>-0.548557</td>\n",
       "      <td>-0.406199</td>\n",
       "      <td>-0.638055</td>\n",
       "      <td>-0.677462</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>1.189286</td>\n",
       "      <td>0.511177</td>\n",
       "      <td>-0.315157</td>\n",
       "      <td>-0.542158</td>\n",
       "      <td>1.229950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.619608</td>\n",
       "      <td>-0.614982</td>\n",
       "      <td>0.162936</td>\n",
       "      <td>-0.441604</td>\n",
       "      <td>-1.249283</td>\n",
       "      <td>-0.610450</td>\n",
       "      <td>-0.384009</td>\n",
       "      <td>-0.458843</td>\n",
       "      <td>-0.620575</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>0.653021</td>\n",
       "      <td>0.838510</td>\n",
       "      <td>-0.526218</td>\n",
       "      <td>-0.723133</td>\n",
       "      <td>1.216534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.754909</td>\n",
       "      <td>1.133054</td>\n",
       "      <td>0.031092</td>\n",
       "      <td>-0.666247</td>\n",
       "      <td>0.095997</td>\n",
       "      <td>-0.583539</td>\n",
       "      <td>-0.506548</td>\n",
       "      <td>-0.631178</td>\n",
       "      <td>-0.651696</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>1.991696</td>\n",
       "      <td>0.385280</td>\n",
       "      <td>-0.297588</td>\n",
       "      <td>0.932424</td>\n",
       "      <td>1.201176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.551957</td>\n",
       "      <td>0.248765</td>\n",
       "      <td>-0.170325</td>\n",
       "      <td>-0.627154</td>\n",
       "      <td>1.354409</td>\n",
       "      <td>-0.832287</td>\n",
       "      <td>-0.413115</td>\n",
       "      <td>-0.559990</td>\n",
       "      <td>-0.510358</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>0.118743</td>\n",
       "      <td>1.807917</td>\n",
       "      <td>-0.494322</td>\n",
       "      <td>-0.548433</td>\n",
       "      <td>1.207205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7746</th>\n",
       "      <td>-2.458495</td>\n",
       "      <td>-0.654605</td>\n",
       "      <td>-0.991760</td>\n",
       "      <td>-0.611932</td>\n",
       "      <td>0.585186</td>\n",
       "      <td>-1.157541</td>\n",
       "      <td>-1.291164</td>\n",
       "      <td>-1.278051</td>\n",
       "      <td>-1.112273</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>-0.685654</td>\n",
       "      <td>1.191021</td>\n",
       "      <td>-0.735149</td>\n",
       "      <td>-0.820115</td>\n",
       "      <td>-2.096560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7747</th>\n",
       "      <td>-2.187892</td>\n",
       "      <td>-1.328126</td>\n",
       "      <td>-1.112066</td>\n",
       "      <td>-0.436683</td>\n",
       "      <td>0.284622</td>\n",
       "      <td>-1.297018</td>\n",
       "      <td>-1.071078</td>\n",
       "      <td>-1.278054</td>\n",
       "      <td>-1.182118</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>-0.149390</td>\n",
       "      <td>-1.263971</td>\n",
       "      <td>-0.852681</td>\n",
       "      <td>-0.803915</td>\n",
       "      <td>-2.093040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7748</th>\n",
       "      <td>-2.187892</td>\n",
       "      <td>-1.548184</td>\n",
       "      <td>-0.887662</td>\n",
       "      <td>-0.255421</td>\n",
       "      <td>-0.454749</td>\n",
       "      <td>-1.274658</td>\n",
       "      <td>-1.094726</td>\n",
       "      <td>-1.278054</td>\n",
       "      <td>-1.182118</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>-0.417522</td>\n",
       "      <td>-1.037356</td>\n",
       "      <td>-0.821213</td>\n",
       "      <td>-0.755095</td>\n",
       "      <td>-2.104553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7749</th>\n",
       "      <td>-2.221718</td>\n",
       "      <td>-1.555342</td>\n",
       "      <td>-0.570780</td>\n",
       "      <td>0.088072</td>\n",
       "      <td>-1.591397</td>\n",
       "      <td>-1.224577</td>\n",
       "      <td>-1.153504</td>\n",
       "      <td>-1.278054</td>\n",
       "      <td>-1.178973</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>-0.417522</td>\n",
       "      <td>-0.269384</td>\n",
       "      <td>-0.779043</td>\n",
       "      <td>-0.719338</td>\n",
       "      <td>-2.074325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7751</th>\n",
       "      <td>2.649126</td>\n",
       "      <td>1.624409</td>\n",
       "      <td>3.044561</td>\n",
       "      <td>6.792009</td>\n",
       "      <td>4.496044</td>\n",
       "      <td>2.291644</td>\n",
       "      <td>2.384303</td>\n",
       "      <td>2.670813</td>\n",
       "      <td>2.669002</td>\n",
       "      <td>11.935477</td>\n",
       "      <td>13.651790</td>\n",
       "      <td>1.991696</td>\n",
       "      <td>1.807917</td>\n",
       "      <td>2.772243</td>\n",
       "      <td>2.861435</td>\n",
       "      <td>1.517935</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7719 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Present_Tmax  LDAPS_RHmax  LDAPS_Tmax_lapse  LDAPS_WS  LDAPS_LH  \\\n",
       "0        -0.361326     0.383078         -0.524889 -0.128382  0.206966   \n",
       "1         0.721084     0.311586          0.080895 -0.646994 -0.314841   \n",
       "2         0.619608    -0.614982          0.162936 -0.441604 -1.249283   \n",
       "3         0.754909     1.133054          0.031092 -0.666247  0.095997   \n",
       "4         0.551957     0.248765         -0.170325 -0.627154  1.354409   \n",
       "...            ...          ...               ...       ...       ...   \n",
       "7746     -2.458495    -0.654605         -0.991760 -0.611932  0.585186   \n",
       "7747     -2.187892    -1.328126         -1.112066 -0.436683  0.284622   \n",
       "7748     -2.187892    -1.548184         -0.887662 -0.255421 -0.454749   \n",
       "7749     -2.221718    -1.555342         -0.570780  0.088072 -1.591397   \n",
       "7751      2.649126     1.624409          3.044561  6.792009  4.496044   \n",
       "\n",
       "      LDAPS_CC1  LDAPS_CC2  LDAPS_CC3  LDAPS_CC4  LDAPS_PPT1  LDAPS_PPT4  \\\n",
       "0     -0.516243  -0.592636  -0.629013  -0.664815   -0.305750   -0.224453   \n",
       "1     -0.548557  -0.406199  -0.638055  -0.677462   -0.305750   -0.224453   \n",
       "2     -0.610450  -0.384009  -0.458843  -0.620575   -0.305750   -0.224453   \n",
       "3     -0.583539  -0.506548  -0.631178  -0.651696   -0.305750   -0.224453   \n",
       "4     -0.832287  -0.413115  -0.559990  -0.510358   -0.305750   -0.224453   \n",
       "...         ...        ...        ...        ...         ...         ...   \n",
       "7746  -1.157541  -1.291164  -1.278051  -1.112273   -0.305750   -0.224453   \n",
       "7747  -1.297018  -1.071078  -1.278054  -1.182118   -0.305750   -0.224453   \n",
       "7748  -1.274658  -1.094726  -1.278054  -1.182118   -0.305750   -0.224453   \n",
       "7749  -1.224577  -1.153504  -1.278054  -1.178973   -0.305750   -0.224453   \n",
       "7751   2.291644   2.384303   2.670813   2.669002   11.935477   13.651790   \n",
       "\n",
       "           lat       lon       DEM     Slope  Solar radiation  \n",
       "0     1.189286 -0.005000  2.772243  1.115004         1.517935  \n",
       "1     1.189286  0.511177 -0.315157 -0.542158         1.229950  \n",
       "2     0.653021  0.838510 -0.526218 -0.723133         1.216534  \n",
       "3     1.991696  0.385280 -0.297588  0.932424         1.201176  \n",
       "4     0.118743  1.807917 -0.494322 -0.548433         1.207205  \n",
       "...        ...       ...       ...       ...              ...  \n",
       "7746 -0.685654  1.191021 -0.735149 -0.820115        -2.096560  \n",
       "7747 -0.149390 -1.263971 -0.852681 -0.803915        -2.093040  \n",
       "7748 -0.417522 -1.037356 -0.821213 -0.755095        -2.104553  \n",
       "7749 -0.417522 -0.269384 -0.779043 -0.719338        -2.074325  \n",
       "7751  1.991696  1.807917  2.772243  2.861435         1.517935  \n",
       "\n",
       "[7719 rows x 16 columns]"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "0      -0.376282\n",
       "1       0.072097\n",
       "2       0.264260\n",
       "3       0.456422\n",
       "4       0.296287\n",
       "          ...   \n",
       "7746   -0.728580\n",
       "7747   -0.632499\n",
       "7748   -0.536418\n",
       "7749   -0.792634\n",
       "7751    2.762374\n",
       "Name: Next_Tmax, Length: 7719, dtype: float64"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 删除后的X,Y -------少了两行\n",
    "X\n",
    "Y "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T03:21:08.371115Z",
     "start_time": "2020-06-16T03:21:08.337137Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE: 0.23653689423387375\n",
      "RMSE: 0.4863505877799201\n"
     ]
    }
   ],
   "source": [
    "# 重新划分数据集\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.3, random_state=1)\n",
    "# 对训练集进行训练\n",
    "lr = LinearRegression()\n",
    "lr.fit(X_train, Y_train) \n",
    "# 对测试集进行预测\n",
    "Y_pred = lr.predict(X_test)\n",
    "MSE = metrics.mean_squared_error(Y_test, Y_pred)\n",
    "RMSE = np.sqrt(metrics.mean_squared_error(Y_test, Y_pred)) \n",
    "print('MSE:',MSE) \n",
    "print('RMSE:',RMSE)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T00:15:27.726012Z",
     "start_time": "2020-06-16T00:15:27.652048Z"
    }
   },
   "outputs": [],
   "source": [
    "# 上一次评估结果\n",
    "# MSE: 0.25266554606897074\n",
    "# RMSE: 0.502658478560713  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T00:15:27.917423Z",
     "start_time": "2020-06-16T00:15:27.728011Z"
    }
   },
   "source": [
    "'''\n",
    "可以看到，该模型的RMSE由原来的0.5降低到了0.48，小幅度的提升了模型的准确率;去除异常值，提高准确度。\n",
    "'''"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 特征筛选"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T03:22:27.527723Z",
     "start_time": "2020-06-16T03:22:27.496745Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Present_Tmax</th>\n",
       "      <th>LDAPS_RHmax</th>\n",
       "      <th>LDAPS_Tmax_lapse</th>\n",
       "      <th>LDAPS_WS</th>\n",
       "      <th>LDAPS_LH</th>\n",
       "      <th>LDAPS_CC1</th>\n",
       "      <th>LDAPS_CC2</th>\n",
       "      <th>LDAPS_CC3</th>\n",
       "      <th>LDAPS_CC4</th>\n",
       "      <th>LDAPS_PPT1</th>\n",
       "      <th>LDAPS_PPT4</th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>DEM</th>\n",
       "      <th>Slope</th>\n",
       "      <th>Solar radiation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.361326</td>\n",
       "      <td>0.383078</td>\n",
       "      <td>-0.524889</td>\n",
       "      <td>-0.128382</td>\n",
       "      <td>0.206966</td>\n",
       "      <td>-0.516243</td>\n",
       "      <td>-0.592636</td>\n",
       "      <td>-0.629013</td>\n",
       "      <td>-0.664815</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>1.189286</td>\n",
       "      <td>-0.005000</td>\n",
       "      <td>2.772243</td>\n",
       "      <td>1.115004</td>\n",
       "      <td>1.517935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.721084</td>\n",
       "      <td>0.311586</td>\n",
       "      <td>0.080895</td>\n",
       "      <td>-0.646994</td>\n",
       "      <td>-0.314841</td>\n",
       "      <td>-0.548557</td>\n",
       "      <td>-0.406199</td>\n",
       "      <td>-0.638055</td>\n",
       "      <td>-0.677462</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>1.189286</td>\n",
       "      <td>0.511177</td>\n",
       "      <td>-0.315157</td>\n",
       "      <td>-0.542158</td>\n",
       "      <td>1.229950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.619608</td>\n",
       "      <td>-0.614982</td>\n",
       "      <td>0.162936</td>\n",
       "      <td>-0.441604</td>\n",
       "      <td>-1.249283</td>\n",
       "      <td>-0.610450</td>\n",
       "      <td>-0.384009</td>\n",
       "      <td>-0.458843</td>\n",
       "      <td>-0.620575</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>0.653021</td>\n",
       "      <td>0.838510</td>\n",
       "      <td>-0.526218</td>\n",
       "      <td>-0.723133</td>\n",
       "      <td>1.216534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.754909</td>\n",
       "      <td>1.133054</td>\n",
       "      <td>0.031092</td>\n",
       "      <td>-0.666247</td>\n",
       "      <td>0.095997</td>\n",
       "      <td>-0.583539</td>\n",
       "      <td>-0.506548</td>\n",
       "      <td>-0.631178</td>\n",
       "      <td>-0.651696</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>1.991696</td>\n",
       "      <td>0.385280</td>\n",
       "      <td>-0.297588</td>\n",
       "      <td>0.932424</td>\n",
       "      <td>1.201176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.551957</td>\n",
       "      <td>0.248765</td>\n",
       "      <td>-0.170325</td>\n",
       "      <td>-0.627154</td>\n",
       "      <td>1.354409</td>\n",
       "      <td>-0.832287</td>\n",
       "      <td>-0.413115</td>\n",
       "      <td>-0.559990</td>\n",
       "      <td>-0.510358</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>0.118743</td>\n",
       "      <td>1.807917</td>\n",
       "      <td>-0.494322</td>\n",
       "      <td>-0.548433</td>\n",
       "      <td>1.207205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7746</th>\n",
       "      <td>-2.458495</td>\n",
       "      <td>-0.654605</td>\n",
       "      <td>-0.991760</td>\n",
       "      <td>-0.611932</td>\n",
       "      <td>0.585186</td>\n",
       "      <td>-1.157541</td>\n",
       "      <td>-1.291164</td>\n",
       "      <td>-1.278051</td>\n",
       "      <td>-1.112273</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>-0.685654</td>\n",
       "      <td>1.191021</td>\n",
       "      <td>-0.735149</td>\n",
       "      <td>-0.820115</td>\n",
       "      <td>-2.096560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7747</th>\n",
       "      <td>-2.187892</td>\n",
       "      <td>-1.328126</td>\n",
       "      <td>-1.112066</td>\n",
       "      <td>-0.436683</td>\n",
       "      <td>0.284622</td>\n",
       "      <td>-1.297018</td>\n",
       "      <td>-1.071078</td>\n",
       "      <td>-1.278054</td>\n",
       "      <td>-1.182118</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>-0.149390</td>\n",
       "      <td>-1.263971</td>\n",
       "      <td>-0.852681</td>\n",
       "      <td>-0.803915</td>\n",
       "      <td>-2.093040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7748</th>\n",
       "      <td>-2.187892</td>\n",
       "      <td>-1.548184</td>\n",
       "      <td>-0.887662</td>\n",
       "      <td>-0.255421</td>\n",
       "      <td>-0.454749</td>\n",
       "      <td>-1.274658</td>\n",
       "      <td>-1.094726</td>\n",
       "      <td>-1.278054</td>\n",
       "      <td>-1.182118</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>-0.417522</td>\n",
       "      <td>-1.037356</td>\n",
       "      <td>-0.821213</td>\n",
       "      <td>-0.755095</td>\n",
       "      <td>-2.104553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7749</th>\n",
       "      <td>-2.221718</td>\n",
       "      <td>-1.555342</td>\n",
       "      <td>-0.570780</td>\n",
       "      <td>0.088072</td>\n",
       "      <td>-1.591397</td>\n",
       "      <td>-1.224577</td>\n",
       "      <td>-1.153504</td>\n",
       "      <td>-1.278054</td>\n",
       "      <td>-1.178973</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>-0.417522</td>\n",
       "      <td>-0.269384</td>\n",
       "      <td>-0.779043</td>\n",
       "      <td>-0.719338</td>\n",
       "      <td>-2.074325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7751</th>\n",
       "      <td>2.649126</td>\n",
       "      <td>1.624409</td>\n",
       "      <td>3.044561</td>\n",
       "      <td>6.792009</td>\n",
       "      <td>4.496044</td>\n",
       "      <td>2.291644</td>\n",
       "      <td>2.384303</td>\n",
       "      <td>2.670813</td>\n",
       "      <td>2.669002</td>\n",
       "      <td>11.935477</td>\n",
       "      <td>13.651790</td>\n",
       "      <td>1.991696</td>\n",
       "      <td>1.807917</td>\n",
       "      <td>2.772243</td>\n",
       "      <td>2.861435</td>\n",
       "      <td>1.517935</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7719 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Present_Tmax  LDAPS_RHmax  LDAPS_Tmax_lapse  LDAPS_WS  LDAPS_LH  \\\n",
       "0        -0.361326     0.383078         -0.524889 -0.128382  0.206966   \n",
       "1         0.721084     0.311586          0.080895 -0.646994 -0.314841   \n",
       "2         0.619608    -0.614982          0.162936 -0.441604 -1.249283   \n",
       "3         0.754909     1.133054          0.031092 -0.666247  0.095997   \n",
       "4         0.551957     0.248765         -0.170325 -0.627154  1.354409   \n",
       "...            ...          ...               ...       ...       ...   \n",
       "7746     -2.458495    -0.654605         -0.991760 -0.611932  0.585186   \n",
       "7747     -2.187892    -1.328126         -1.112066 -0.436683  0.284622   \n",
       "7748     -2.187892    -1.548184         -0.887662 -0.255421 -0.454749   \n",
       "7749     -2.221718    -1.555342         -0.570780  0.088072 -1.591397   \n",
       "7751      2.649126     1.624409          3.044561  6.792009  4.496044   \n",
       "\n",
       "      LDAPS_CC1  LDAPS_CC2  LDAPS_CC3  LDAPS_CC4  LDAPS_PPT1  LDAPS_PPT4  \\\n",
       "0     -0.516243  -0.592636  -0.629013  -0.664815   -0.305750   -0.224453   \n",
       "1     -0.548557  -0.406199  -0.638055  -0.677462   -0.305750   -0.224453   \n",
       "2     -0.610450  -0.384009  -0.458843  -0.620575   -0.305750   -0.224453   \n",
       "3     -0.583539  -0.506548  -0.631178  -0.651696   -0.305750   -0.224453   \n",
       "4     -0.832287  -0.413115  -0.559990  -0.510358   -0.305750   -0.224453   \n",
       "...         ...        ...        ...        ...         ...         ...   \n",
       "7746  -1.157541  -1.291164  -1.278051  -1.112273   -0.305750   -0.224453   \n",
       "7747  -1.297018  -1.071078  -1.278054  -1.182118   -0.305750   -0.224453   \n",
       "7748  -1.274658  -1.094726  -1.278054  -1.182118   -0.305750   -0.224453   \n",
       "7749  -1.224577  -1.153504  -1.278054  -1.178973   -0.305750   -0.224453   \n",
       "7751   2.291644   2.384303   2.670813   2.669002   11.935477   13.651790   \n",
       "\n",
       "           lat       lon       DEM     Slope  Solar radiation  \n",
       "0     1.189286 -0.005000  2.772243  1.115004         1.517935  \n",
       "1     1.189286  0.511177 -0.315157 -0.542158         1.229950  \n",
       "2     0.653021  0.838510 -0.526218 -0.723133         1.216534  \n",
       "3     1.991696  0.385280 -0.297588  0.932424         1.201176  \n",
       "4     0.118743  1.807917 -0.494322 -0.548433         1.207205  \n",
       "...        ...       ...       ...       ...              ...  \n",
       "7746 -0.685654  1.191021 -0.735149 -0.820115        -2.096560  \n",
       "7747 -0.149390 -1.263971 -0.852681 -0.803915        -2.093040  \n",
       "7748 -0.417522 -1.037356 -0.821213 -0.755095        -2.104553  \n",
       "7749 -0.417522 -0.269384 -0.779043 -0.719338        -2.074325  \n",
       "7751  1.991696  1.807917  2.772243  2.861435         1.517935  \n",
       "\n",
       "[7719 rows x 16 columns]"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T03:22:30.962802Z",
     "start_time": "2020-06-16T03:22:30.906836Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 特征筛选，选出和样本标签之间存在显著相关性（p<0.05)的特征，从而过滤掉对模型预测贡献不大的特征\n",
    "from sklearn.feature_selection import f_regression\n",
    "\n",
    "F,p=f_regression(X,Y)  # 一个特征对应一个F值和p值\n",
    "k=F.shape[0] - (p>0.05).sum() # 去掉p值<=0.05的特征，k就是剩下的特征数量\n",
    "k "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T03:22:33.930084Z",
     "start_time": "2020-06-16T03:22:33.914094Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7719, 14)"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "array([[-0.36132577,  0.38307796, -0.52488856, ...,  1.18928568,\n",
       "         2.77224286,  1.11500407],\n",
       "       [ 0.72108401,  0.31158619,  0.08089519, ...,  1.18928568,\n",
       "        -0.31515742, -0.54215762],\n",
       "       [ 0.61960809, -0.61498177,  0.16293647, ...,  0.65302103,\n",
       "        -0.52621832, -0.7231326 ],\n",
       "       ...,\n",
       "       [-2.18789227, -1.54818379, -0.88766178, ..., -0.41752209,\n",
       "        -0.82121274, -0.75509512],\n",
       "       [-2.22171758, -1.55534234, -0.57077984, ..., -0.41752209,\n",
       "        -0.77904331, -0.71933797],\n",
       "       [ 2.64912642,  1.62440928,  3.04456067, ...,  1.99169648,\n",
       "         2.77224286,  2.86143459]])"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# F检验筛选特征\n",
    "from sklearn.feature_selection import SelectKBest\n",
    "\n",
    "X_f=SelectKBest(f_regression,k=k).fit_transform(X,Y) \n",
    "X_f.shape    # 过滤掉了两个特征\n",
    "X_f"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T03:22:38.343703Z",
     "start_time": "2020-06-16T03:22:38.336692Z"
    }
   },
   "outputs": [],
   "source": [
    "xtrain,xtest,ytrain,ytest=train_test_split(X_f,Y,test_size=0.3, random_state=1) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T03:22:40.510995Z",
     "start_time": "2020-06-16T03:22:40.504012Z"
    }
   },
   "outputs": [],
   "source": [
    "# # 实例化并训练模型\n",
    "lr=LinearRegression().fit(xtrain,ytrain) \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T03:22:45.574369Z",
     "start_time": "2020-06-16T03:22:45.562377Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.738888469431397, 0.7431917103560932)"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 测试集上的评分（R²）\n",
    "lr.score(xtrain,ytrain),lr.score(xtest,ytest) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T03:22:50.126024Z",
     "start_time": "2020-06-16T03:22:50.117031Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE: 0.24095547445500337\n",
      "RMSE: 0.4908721569360024\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "ypred = lr.predict(xtest) \n",
    "MSE = metrics.mean_squared_error(ytest, ypred)\n",
    "RMSE = np.sqrt(metrics.mean_squared_error(ytest, ypred)) \n",
    "print('MSE:',MSE) \n",
    "print('RMSE:',RMSE) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## PCA降维\n",
    "\n",
    "主成分分析的目的是从现有的特征中重建新的特征，新的特征剔除了原有特征中的冗余信息，因此更有区分度。\n",
    "\n",
    "主成分分析的结果是得到新的特征，而不是简单地舍弃原来的特征列表中的一些特征。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T03:22:58.610715Z",
     "start_time": "2020-06-16T03:22:58.587725Z"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "\n",
    "pca=PCA(n_components=0.97).fit(X,Y) \n",
    "X_pca=pca.transform(X)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T03:23:04.873414Z",
     "start_time": "2020-06-16T03:23:04.832436Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "      <td>0.750534</td>\n",
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       "      <td>-0.976939</td>\n",
       "      <td>-0.864368</td>\n",
       "      <td>-0.227469</td>\n",
       "      <td>1.047950</td>\n",
       "      <td>0.419490</td>\n",
       "      <td>-0.490863</td>\n",
       "      <td>-0.845073</td>\n",
       "      <td>-0.677817</td>\n",
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       "      <th>4</th>\n",
       "      <td>-1.181040</td>\n",
       "      <td>0.132213</td>\n",
       "      <td>1.700762</td>\n",
       "      <td>0.864334</td>\n",
       "      <td>0.305876</td>\n",
       "      <td>0.042785</td>\n",
       "      <td>-0.695446</td>\n",
       "      <td>-0.530648</td>\n",
       "      <td>0.390678</td>\n",
       "      <td>-1.373559</td>\n",
       "      <td>-0.684244</td>\n",
       "      <td>-0.126623</td>\n",
       "      <td>-0.934499</td>\n",
       "      <td>0.187668</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <th>7714</th>\n",
       "      <td>-1.828045</td>\n",
       "      <td>0.204511</td>\n",
       "      <td>0.087075</td>\n",
       "      <td>-0.137277</td>\n",
       "      <td>-3.611235</td>\n",
       "      <td>-0.232090</td>\n",
       "      <td>0.198096</td>\n",
       "      <td>1.399704</td>\n",
       "      <td>0.710092</td>\n",
       "      <td>-1.356931</td>\n",
       "      <td>-0.861351</td>\n",
       "      <td>-0.014907</td>\n",
       "      <td>-0.133469</td>\n",
       "      <td>-0.036297</td>\n",
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       "      <th>7715</th>\n",
       "      <td>-1.917145</td>\n",
       "      <td>-0.185958</td>\n",
       "      <td>-0.908207</td>\n",
       "      <td>-0.975988</td>\n",
       "      <td>-3.522399</td>\n",
       "      <td>0.521996</td>\n",
       "      <td>-0.256674</td>\n",
       "      <td>1.343472</td>\n",
       "      <td>-0.246521</td>\n",
       "      <td>0.404696</td>\n",
       "      <td>-0.731927</td>\n",
       "      <td>0.547065</td>\n",
       "      <td>-0.274555</td>\n",
       "      <td>-0.114590</td>\n",
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       "    <tr>\n",
       "      <th>7716</th>\n",
       "      <td>-1.945816</td>\n",
       "      <td>-0.417947</td>\n",
       "      <td>-1.306099</td>\n",
       "      <td>-1.002492</td>\n",
       "      <td>-3.298725</td>\n",
       "      <td>-0.026787</td>\n",
       "      <td>-0.182500</td>\n",
       "      <td>1.672442</td>\n",
       "      <td>-0.081839</td>\n",
       "      <td>0.324312</td>\n",
       "      <td>-0.520419</td>\n",
       "      <td>0.345995</td>\n",
       "      <td>-0.210014</td>\n",
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       "    <tr>\n",
       "      <th>7717</th>\n",
       "      <td>-1.864801</td>\n",
       "      <td>-0.594864</td>\n",
       "      <td>-1.441804</td>\n",
       "      <td>-0.743734</td>\n",
       "      <td>-3.012536</td>\n",
       "      <td>-1.071030</td>\n",
       "      <td>-0.124629</td>\n",
       "      <td>2.227065</td>\n",
       "      <td>0.116584</td>\n",
       "      <td>0.182574</td>\n",
       "      <td>-0.114871</td>\n",
       "      <td>-0.153969</td>\n",
       "      <td>-0.112684</td>\n",
       "      <td>-0.181380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7718</th>\n",
       "      <td>8.784503</td>\n",
       "      <td>3.163045</td>\n",
       "      <td>4.467119</td>\n",
       "      <td>4.805559</td>\n",
       "      <td>9.278716</td>\n",
       "      <td>8.369438</td>\n",
       "      <td>5.980771</td>\n",
       "      <td>9.079706</td>\n",
       "      <td>6.199409</td>\n",
       "      <td>3.802075</td>\n",
       "      <td>0.179508</td>\n",
       "      <td>1.663953</td>\n",
       "      <td>-0.335985</td>\n",
       "      <td>0.833460</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7719 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            0         1         2         3         4         5         6   \\\n",
       "0    -0.188875  3.105274 -0.174420  0.232023  0.614357 -0.712495 -1.301660   \n",
       "1    -1.041580 -0.102778  1.236504  0.435599  0.429038 -0.683036 -1.097555   \n",
       "2    -1.122112 -0.891416  0.471348  0.462210  0.594114 -1.437138 -1.028615   \n",
       "3    -0.787025  1.227941  1.628529  0.750534  0.480516 -0.491080 -0.976939   \n",
       "4    -1.181040  0.132213  1.700762  0.864334  0.305876  0.042785 -0.695446   \n",
       "...        ...       ...       ...       ...       ...       ...       ...   \n",
       "7714 -1.828045  0.204511  0.087075 -0.137277 -3.611235 -0.232090  0.198096   \n",
       "7715 -1.917145 -0.185958 -0.908207 -0.975988 -3.522399  0.521996 -0.256674   \n",
       "7716 -1.945816 -0.417947 -1.306099 -1.002492 -3.298725 -0.026787 -0.182500   \n",
       "7717 -1.864801 -0.594864 -1.441804 -0.743734 -3.012536 -1.071030 -0.124629   \n",
       "7718  8.784503  3.163045  4.467119  4.805559  9.278716  8.369438  5.980771   \n",
       "\n",
       "            7         8         9         10        11        12        13  \n",
       "0    -0.395921  0.169397  0.661308 -0.334142  0.350072 -0.427288  1.102016  \n",
       "1    -0.397473 -0.026081  0.502019  0.201790 -0.256293 -0.824149  0.247166  \n",
       "2     0.193530  0.127245  0.225650  0.100329 -0.208413 -0.926234  0.177910  \n",
       "3    -0.864368 -0.227469  1.047950  0.419490 -0.490863 -0.845073 -0.677817  \n",
       "4    -0.530648  0.390678 -1.373559 -0.684244 -0.126623 -0.934499  0.187668  \n",
       "...        ...       ...       ...       ...       ...       ...       ...  \n",
       "7714  1.399704  0.710092 -1.356931 -0.861351 -0.014907 -0.133469 -0.036297  \n",
       "7715  1.343472 -0.246521  0.404696 -0.731927  0.547065 -0.274555 -0.114590  \n",
       "7716  1.672442 -0.081839  0.324312 -0.520419  0.345995 -0.210014 -0.158449  \n",
       "7717  2.227065  0.116584  0.182574 -0.114871 -0.153969 -0.112684 -0.181380  \n",
       "7718  9.079706  6.199409  3.802075  0.179508  1.663953 -0.335985  0.833460  \n",
       "\n",
       "[7719 rows x 14 columns]"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_pca=pd.DataFrame(X_pca)\n",
    "X_pca"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T03:23:07.297511Z",
     "start_time": "2020-06-16T03:23:07.288516Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      -0.376282\n",
       "1       0.072097\n",
       "2       0.264260\n",
       "3       0.456422\n",
       "4       0.296287\n",
       "          ...   \n",
       "7746   -0.728580\n",
       "7747   -0.632499\n",
       "7748   -0.536418\n",
       "7749   -0.792634\n",
       "7751    2.762374\n",
       "Name: Next_Tmax, Length: 7719, dtype: float64"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T03:23:10.305510Z",
     "start_time": "2020-06-16T03:23:10.277525Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "      <th>13</th>\n",
       "      <th>14</th>\n",
       "      <th>15</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.222742</td>\n",
       "      <td>0.263311</td>\n",
       "      <td>-0.354324</td>\n",
       "      <td>0.201683</td>\n",
       "      <td>-0.104739</td>\n",
       "      <td>0.393294</td>\n",
       "      <td>0.422638</td>\n",
       "      <td>0.399707</td>\n",
       "      <td>0.344365</td>\n",
       "      <td>0.199209</td>\n",
       "      <td>0.161564</td>\n",
       "      <td>0.024391</td>\n",
       "      <td>-0.007817</td>\n",
       "      <td>0.075389</td>\n",
       "      <td>0.068689</td>\n",
       "      <td>0.123937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.176182</td>\n",
       "      <td>0.264489</td>\n",
       "      <td>-0.113884</td>\n",
       "      <td>0.138779</td>\n",
       "      <td>0.255222</td>\n",
       "      <td>-0.076576</td>\n",
       "      <td>-0.145124</td>\n",
       "      <td>-0.186183</td>\n",
       "      <td>-0.187085</td>\n",
       "      <td>0.005605</td>\n",
       "      <td>-0.060720</td>\n",
       "      <td>0.174841</td>\n",
       "      <td>0.062611</td>\n",
       "      <td>0.572606</td>\n",
       "      <td>0.582067</td>\n",
       "      <td>0.012661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.035115</td>\n",
       "      <td>0.371731</td>\n",
       "      <td>0.122479</td>\n",
       "      <td>-0.090039</td>\n",
       "      <td>0.359309</td>\n",
       "      <td>0.196599</td>\n",
       "      <td>0.050196</td>\n",
       "      <td>-0.139835</td>\n",
       "      <td>-0.191900</td>\n",
       "      <td>0.303063</td>\n",
       "      <td>-0.086435</td>\n",
       "      <td>0.464752</td>\n",
       "      <td>0.363850</td>\n",
       "      <td>-0.293949</td>\n",
       "      <td>-0.242831</td>\n",
       "      <td>0.133229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.145043</td>\n",
       "      <td>-0.114328</td>\n",
       "      <td>0.010787</td>\n",
       "      <td>-0.022730</td>\n",
       "      <td>0.069970</td>\n",
       "      <td>-0.267264</td>\n",
       "      <td>-0.078085</td>\n",
       "      <td>0.237171</td>\n",
       "      <td>0.337363</td>\n",
       "      <td>-0.364178</td>\n",
       "      <td>0.459535</td>\n",
       "      <td>0.384470</td>\n",
       "      <td>0.459891</td>\n",
       "      <td>0.026564</td>\n",
       "      <td>0.064236</td>\n",
       "      <td>-0.069460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.553570</td>\n",
       "      <td>-0.119346</td>\n",
       "      <td>0.414334</td>\n",
       "      <td>0.270975</td>\n",
       "      <td>-0.021542</td>\n",
       "      <td>0.071159</td>\n",
       "      <td>0.067682</td>\n",
       "      <td>0.067230</td>\n",
       "      <td>0.081974</td>\n",
       "      <td>0.203411</td>\n",
       "      <td>0.014358</td>\n",
       "      <td>-0.091487</td>\n",
       "      <td>0.033391</td>\n",
       "      <td>0.157431</td>\n",
       "      <td>0.178319</td>\n",
       "      <td>0.552467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.163904</td>\n",
       "      <td>0.208568</td>\n",
       "      <td>-0.055769</td>\n",
       "      <td>0.166646</td>\n",
       "      <td>0.630398</td>\n",
       "      <td>-0.084256</td>\n",
       "      <td>-0.115130</td>\n",
       "      <td>-0.009862</td>\n",
       "      <td>0.103554</td>\n",
       "      <td>0.013371</td>\n",
       "      <td>0.428871</td>\n",
       "      <td>-0.210514</td>\n",
       "      <td>-0.458194</td>\n",
       "      <td>-0.123691</td>\n",
       "      <td>-0.101349</td>\n",
       "      <td>-0.078992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.365585</td>\n",
       "      <td>-0.052028</td>\n",
       "      <td>0.078648</td>\n",
       "      <td>-0.087867</td>\n",
       "      <td>0.051706</td>\n",
       "      <td>0.065798</td>\n",
       "      <td>0.109443</td>\n",
       "      <td>0.116339</td>\n",
       "      <td>0.064194</td>\n",
       "      <td>0.446661</td>\n",
       "      <td>-0.055199</td>\n",
       "      <td>-0.137450</td>\n",
       "      <td>0.193075</td>\n",
       "      <td>0.096424</td>\n",
       "      <td>0.172967</td>\n",
       "      <td>-0.717746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>-0.265292</td>\n",
       "      <td>-0.279226</td>\n",
       "      <td>0.006281</td>\n",
       "      <td>0.658992</td>\n",
       "      <td>-0.181603</td>\n",
       "      <td>-0.010895</td>\n",
       "      <td>-0.189539</td>\n",
       "      <td>-0.228418</td>\n",
       "      <td>-0.142294</td>\n",
       "      <td>0.329392</td>\n",
       "      <td>0.325918</td>\n",
       "      <td>0.064108</td>\n",
       "      <td>0.143243</td>\n",
       "      <td>-0.070112</td>\n",
       "      <td>-0.129384</td>\n",
       "      <td>-0.122030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>-0.260557</td>\n",
       "      <td>0.114002</td>\n",
       "      <td>0.184736</td>\n",
       "      <td>-0.514467</td>\n",
       "      <td>-0.118458</td>\n",
       "      <td>0.006525</td>\n",
       "      <td>-0.093250</td>\n",
       "      <td>-0.117852</td>\n",
       "      <td>-0.031030</td>\n",
       "      <td>0.280630</td>\n",
       "      <td>0.520935</td>\n",
       "      <td>-0.379582</td>\n",
       "      <td>0.203918</td>\n",
       "      <td>0.091311</td>\n",
       "      <td>0.059847</td>\n",
       "      <td>0.187024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.039571</td>\n",
       "      <td>-0.005249</td>\n",
       "      <td>0.117088</td>\n",
       "      <td>-0.231484</td>\n",
       "      <td>-0.297357</td>\n",
       "      <td>-0.123173</td>\n",
       "      <td>-0.064410</td>\n",
       "      <td>0.011790</td>\n",
       "      <td>0.075488</td>\n",
       "      <td>0.310984</td>\n",
       "      <td>0.169668</td>\n",
       "      <td>0.599610</td>\n",
       "      <td>-0.569295</td>\n",
       "      <td>0.044760</td>\n",
       "      <td>0.071209</td>\n",
       "      <td>-0.036481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.229190</td>\n",
       "      <td>0.369807</td>\n",
       "      <td>0.186680</td>\n",
       "      <td>0.122171</td>\n",
       "      <td>-0.328840</td>\n",
       "      <td>0.235760</td>\n",
       "      <td>0.282023</td>\n",
       "      <td>-0.140312</td>\n",
       "      <td>-0.417206</td>\n",
       "      <td>-0.404072</td>\n",
       "      <td>0.329369</td>\n",
       "      <td>0.011250</td>\n",
       "      <td>-0.061243</td>\n",
       "      <td>-0.032764</td>\n",
       "      <td>0.053543</td>\n",
       "      <td>-0.210616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>-0.036390</td>\n",
       "      <td>-0.638793</td>\n",
       "      <td>-0.067746</td>\n",
       "      <td>-0.205118</td>\n",
       "      <td>0.325155</td>\n",
       "      <td>0.465504</td>\n",
       "      <td>0.256466</td>\n",
       "      <td>-0.075083</td>\n",
       "      <td>-0.236228</td>\n",
       "      <td>-0.094371</td>\n",
       "      <td>0.183414</td>\n",
       "      <td>0.155599</td>\n",
       "      <td>-0.077864</td>\n",
       "      <td>0.138497</td>\n",
       "      <td>0.042510</td>\n",
       "      <td>0.022637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>-0.352690</td>\n",
       "      <td>0.055647</td>\n",
       "      <td>0.669917</td>\n",
       "      <td>0.084548</td>\n",
       "      <td>0.103422</td>\n",
       "      <td>0.320851</td>\n",
       "      <td>-0.078004</td>\n",
       "      <td>-0.062740</td>\n",
       "      <td>0.417550</td>\n",
       "      <td>-0.182148</td>\n",
       "      <td>-0.123814</td>\n",
       "      <td>0.028249</td>\n",
       "      <td>-0.074508</td>\n",
       "      <td>0.139466</td>\n",
       "      <td>-0.074121</td>\n",
       "      <td>-0.196884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.043159</td>\n",
       "      <td>0.060647</td>\n",
       "      <td>0.016026</td>\n",
       "      <td>0.005236</td>\n",
       "      <td>0.019374</td>\n",
       "      <td>-0.188307</td>\n",
       "      <td>0.143595</td>\n",
       "      <td>0.126086</td>\n",
       "      <td>-0.141133</td>\n",
       "      <td>0.039357</td>\n",
       "      <td>0.012209</td>\n",
       "      <td>0.014843</td>\n",
       "      <td>0.011680</td>\n",
       "      <td>0.666280</td>\n",
       "      <td>-0.675011</td>\n",
       "      <td>-0.016761</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          0         1         2         3         4         5         6   \\\n",
       "0  -0.222742  0.263311 -0.354324  0.201683 -0.104739  0.393294  0.422638   \n",
       "1  -0.176182  0.264489 -0.113884  0.138779  0.255222 -0.076576 -0.145124   \n",
       "2   0.035115  0.371731  0.122479 -0.090039  0.359309  0.196599  0.050196   \n",
       "3   0.145043 -0.114328  0.010787 -0.022730  0.069970 -0.267264 -0.078085   \n",
       "4   0.553570 -0.119346  0.414334  0.270975 -0.021542  0.071159  0.067682   \n",
       "5   0.163904  0.208568 -0.055769  0.166646  0.630398 -0.084256 -0.115130   \n",
       "6   0.365585 -0.052028  0.078648 -0.087867  0.051706  0.065798  0.109443   \n",
       "7  -0.265292 -0.279226  0.006281  0.658992 -0.181603 -0.010895 -0.189539   \n",
       "8  -0.260557  0.114002  0.184736 -0.514467 -0.118458  0.006525 -0.093250   \n",
       "9   0.039571 -0.005249  0.117088 -0.231484 -0.297357 -0.123173 -0.064410   \n",
       "10  0.229190  0.369807  0.186680  0.122171 -0.328840  0.235760  0.282023   \n",
       "11 -0.036390 -0.638793 -0.067746 -0.205118  0.325155  0.465504  0.256466   \n",
       "12 -0.352690  0.055647  0.669917  0.084548  0.103422  0.320851 -0.078004   \n",
       "13  0.043159  0.060647  0.016026  0.005236  0.019374 -0.188307  0.143595   \n",
       "\n",
       "          7         8         9         10        11        12        13  \\\n",
       "0   0.399707  0.344365  0.199209  0.161564  0.024391 -0.007817  0.075389   \n",
       "1  -0.186183 -0.187085  0.005605 -0.060720  0.174841  0.062611  0.572606   \n",
       "2  -0.139835 -0.191900  0.303063 -0.086435  0.464752  0.363850 -0.293949   \n",
       "3   0.237171  0.337363 -0.364178  0.459535  0.384470  0.459891  0.026564   \n",
       "4   0.067230  0.081974  0.203411  0.014358 -0.091487  0.033391  0.157431   \n",
       "5  -0.009862  0.103554  0.013371  0.428871 -0.210514 -0.458194 -0.123691   \n",
       "6   0.116339  0.064194  0.446661 -0.055199 -0.137450  0.193075  0.096424   \n",
       "7  -0.228418 -0.142294  0.329392  0.325918  0.064108  0.143243 -0.070112   \n",
       "8  -0.117852 -0.031030  0.280630  0.520935 -0.379582  0.203918  0.091311   \n",
       "9   0.011790  0.075488  0.310984  0.169668  0.599610 -0.569295  0.044760   \n",
       "10 -0.140312 -0.417206 -0.404072  0.329369  0.011250 -0.061243 -0.032764   \n",
       "11 -0.075083 -0.236228 -0.094371  0.183414  0.155599 -0.077864  0.138497   \n",
       "12 -0.062740  0.417550 -0.182148 -0.123814  0.028249 -0.074508  0.139466   \n",
       "13  0.126086 -0.141133  0.039357  0.012209  0.014843  0.011680  0.666280   \n",
       "\n",
       "          14        15  \n",
       "0   0.068689  0.123937  \n",
       "1   0.582067  0.012661  \n",
       "2  -0.242831  0.133229  \n",
       "3   0.064236 -0.069460  \n",
       "4   0.178319  0.552467  \n",
       "5  -0.101349 -0.078992  \n",
       "6   0.172967 -0.717746  \n",
       "7  -0.129384 -0.122030  \n",
       "8   0.059847  0.187024  \n",
       "9   0.071209 -0.036481  \n",
       "10  0.053543 -0.210616  \n",
       "11  0.042510  0.022637  \n",
       "12 -0.074121 -0.196884  \n",
       "13 -0.675011 -0.016761  "
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "components=pca.components_   #14个新特征的特征向量(每一行) \n",
    "components=pd.DataFrame(components) \n",
    "components"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T03:23:51.506451Z",
     "start_time": "2020-06-16T03:23:51.497454Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.2524355 , 0.12934125, 0.0891994 , 0.08255682, 0.07232587,\n",
       "       0.06844881, 0.05650669, 0.05073909, 0.05063498, 0.04080831,\n",
       "       0.03431659, 0.0232422 , 0.01804316, 0.01314993])"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca.explained_variance_ratio_ #主成分的解释方差百分比"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T03:23:54.442229Z",
     "start_time": "2020-06-16T03:23:54.432222Z"
    }
   },
   "outputs": [],
   "source": [
    "# 重新划分训练集和测试集\n",
    "Xtrain,Xtest,Ytrain,Ytest=train_test_split(X_pca,Y, test_size=0.3, random_state=1) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T03:23:57.857425Z",
     "start_time": "2020-06-16T03:23:57.848431Z"
    }
   },
   "outputs": [],
   "source": [
    "# # 实例化并训练模型\n",
    "lr=LinearRegression().fit(Xtrain,Ytrain) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T03:23:59.855182Z",
     "start_time": "2020-06-16T03:23:59.839193Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.731342230893874, 0.731342230893874)"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 模型在训练集和测试机上的评分（R²）\n",
    "lr.score(Xtrain,Ytrain),lr.score(Xtrain,Ytrain)  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 随机森林回归 RandomForestRegression（20分）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T00:36:37.843504Z",
     "start_time": "2020-06-16T00:15:30.474184Z"
    }
   },
   "outputs": [],
   "source": [
    "# 1.使用集成算法-随机森林回归器\n",
    "# 2.使用网格搜索寻找主要参数的最优取值组合\n",
    "# 3.使用去除异常值后的训练集数据训练模型（15分）\n",
    "from sklearn.ensemble import RandomForestRegressor \n",
    "\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [],
   "source": [
    "GS1=RandomForestRegressor(n_jobs = -1,random_state=0)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, estimator=RandomForestRegressor(),\n",
       "             param_grid={'max_depth': range(6, 10)})"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "#测试参数\n",
    "p = {\n",
    "    \n",
    "    'max_depth': range(6,10),\n",
    "    \n",
    "    \n",
    "}\n",
    "\n",
    "model  = RandomForestRegressor()\n",
    "\n",
    "GS = GridSearchCV(model,p,cv =5 )\n",
    "GS.fit(Xtrain,Ytrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T00:39:36.195735Z",
     "start_time": "2020-06-16T00:39:36.191739Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'max_depth': 9}"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 查看最优参数组合\n",
    "GS.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T00:40:27.950412Z",
     "start_time": "2020-06-16T00:40:22.666442Z"
    }
   },
   "outputs": [],
   "source": [
    "# 用上面得到的最优参数组合重新实例化模型\n",
    "rfr=RandomForestRegressor(n_estimators=181,\n",
    "                         max_depth=9,).fit(X_train,Y_train)   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T00:40:30.380572Z",
     "start_time": "2020-06-16T00:40:30.207691Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.9135129787609856, 0.8454799017537702)"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfr.score(X_train,Y_train),rfr.score(X_test,Y_test)   # 随机森林回归算法的score返回的是R²，并不是mse(尽管实例化时用的criterion='mse') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T00:40:48.947100Z",
     "start_time": "2020-06-16T00:40:39.499061Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.4178795169010945"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# # 使用交叉验证---随机森林回归器在测试集上的MSE平均得分（5分）\n",
    "from sklearn.model_selection import cross_val_predict\n",
    "cross_val_score(rfr,X,Y,scoring='neg_mean_absolute_error').mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T00:36:54.809803Z",
     "start_time": "2020-06-16T00:36:54.803807Z"
    },
    "scrolled": false
   },
   "source": [
    "'''\n",
    "可以看出：无论是在训练集上还是测试集上，随机森林的的表现均优于LinearRegression,Lasson和Ridge：\n",
    "训练集上的MSE由原来的0.25降到了0.15，R²由原来的0.75上升到了0.85。\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Present_Tmax</th>\n",
       "      <th>LDAPS_RHmax</th>\n",
       "      <th>LDAPS_Tmax_lapse</th>\n",
       "      <th>LDAPS_WS</th>\n",
       "      <th>LDAPS_LH</th>\n",
       "      <th>LDAPS_CC1</th>\n",
       "      <th>LDAPS_CC2</th>\n",
       "      <th>LDAPS_CC3</th>\n",
       "      <th>LDAPS_CC4</th>\n",
       "      <th>LDAPS_PPT1</th>\n",
       "      <th>LDAPS_PPT4</th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>DEM</th>\n",
       "      <th>Slope</th>\n",
       "      <th>Solar radiation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4867</th>\n",
       "      <td>1.837319</td>\n",
       "      <td>0.008303</td>\n",
       "      <td>0.893636</td>\n",
       "      <td>-0.640934</td>\n",
       "      <td>1.733691</td>\n",
       "      <td>-1.365341</td>\n",
       "      <td>-1.365146</td>\n",
       "      <td>-1.063160</td>\n",
       "      <td>-0.055770</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>-1.221919</td>\n",
       "      <td>0.410460</td>\n",
       "      <td>-0.099918</td>\n",
       "      <td>-0.018790</td>\n",
       "      <td>1.054466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4126</th>\n",
       "      <td>0.585783</td>\n",
       "      <td>0.725294</td>\n",
       "      <td>0.895328</td>\n",
       "      <td>-0.713731</td>\n",
       "      <td>0.150235</td>\n",
       "      <td>-0.835744</td>\n",
       "      <td>-0.119614</td>\n",
       "      <td>-0.193429</td>\n",
       "      <td>-0.834085</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>1.189286</td>\n",
       "      <td>0.511177</td>\n",
       "      <td>-0.315157</td>\n",
       "      <td>-0.542158</td>\n",
       "      <td>-0.416905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4361</th>\n",
       "      <td>-0.056898</td>\n",
       "      <td>-1.079991</td>\n",
       "      <td>-0.451831</td>\n",
       "      <td>-0.225430</td>\n",
       "      <td>-1.271137</td>\n",
       "      <td>0.361864</td>\n",
       "      <td>0.349921</td>\n",
       "      <td>0.342862</td>\n",
       "      <td>1.278165</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>0.118743</td>\n",
       "      <td>-0.042770</td>\n",
       "      <td>1.294304</td>\n",
       "      <td>-0.484508</td>\n",
       "      <td>-0.988697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3605</th>\n",
       "      <td>-0.158374</td>\n",
       "      <td>0.052453</td>\n",
       "      <td>0.415613</td>\n",
       "      <td>-0.603475</td>\n",
       "      <td>-0.870337</td>\n",
       "      <td>0.482337</td>\n",
       "      <td>0.654817</td>\n",
       "      <td>-0.293343</td>\n",
       "      <td>-0.397379</td>\n",
       "      <td>-0.305342</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>-0.685654</td>\n",
       "      <td>0.637074</td>\n",
       "      <td>-0.133199</td>\n",
       "      <td>-0.810993</td>\n",
       "      <td>0.725437</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6637</th>\n",
       "      <td>0.146054</td>\n",
       "      <td>-0.004252</td>\n",
       "      <td>0.764463</td>\n",
       "      <td>-0.471555</td>\n",
       "      <td>-0.421035</td>\n",
       "      <td>-0.711618</td>\n",
       "      <td>-0.673577</td>\n",
       "      <td>-0.707840</td>\n",
       "      <td>-0.905956</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>0.653021</td>\n",
       "      <td>1.153252</td>\n",
       "      <td>-0.037504</td>\n",
       "      <td>1.043125</td>\n",
       "      <td>0.803594</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>905</th>\n",
       "      <td>0.247530</td>\n",
       "      <td>0.650272</td>\n",
       "      <td>0.440318</td>\n",
       "      <td>0.121206</td>\n",
       "      <td>-0.631110</td>\n",
       "      <td>-0.459710</td>\n",
       "      <td>-0.042247</td>\n",
       "      <td>0.527967</td>\n",
       "      <td>-0.678392</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>-0.685654</td>\n",
       "      <td>0.637074</td>\n",
       "      <td>-0.133199</td>\n",
       "      <td>-0.810993</td>\n",
       "      <td>-0.091289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5197</th>\n",
       "      <td>0.721084</td>\n",
       "      <td>-1.888432</td>\n",
       "      <td>0.933176</td>\n",
       "      <td>-0.161507</td>\n",
       "      <td>0.493303</td>\n",
       "      <td>-0.438405</td>\n",
       "      <td>0.569373</td>\n",
       "      <td>-0.010130</td>\n",
       "      <td>0.339327</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>-0.149390</td>\n",
       "      <td>-1.263971</td>\n",
       "      <td>-0.852681</td>\n",
       "      <td>-0.803915</td>\n",
       "      <td>0.594264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3982</th>\n",
       "      <td>0.146054</td>\n",
       "      <td>0.567746</td>\n",
       "      <td>-0.230874</td>\n",
       "      <td>0.725265</td>\n",
       "      <td>-0.341861</td>\n",
       "      <td>0.753035</td>\n",
       "      <td>1.509653</td>\n",
       "      <td>0.771193</td>\n",
       "      <td>-0.642142</td>\n",
       "      <td>-0.260963</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>-1.490051</td>\n",
       "      <td>-1.024766</td>\n",
       "      <td>-0.172266</td>\n",
       "      <td>0.223191</td>\n",
       "      <td>-0.064295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>235</th>\n",
       "      <td>-0.699579</td>\n",
       "      <td>0.694980</td>\n",
       "      <td>0.507066</td>\n",
       "      <td>0.092346</td>\n",
       "      <td>-0.701005</td>\n",
       "      <td>2.059803</td>\n",
       "      <td>1.112615</td>\n",
       "      <td>0.543855</td>\n",
       "      <td>0.485321</td>\n",
       "      <td>0.713948</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>-0.149390</td>\n",
       "      <td>1.178432</td>\n",
       "      <td>-0.611095</td>\n",
       "      <td>-0.462470</td>\n",
       "      <td>1.057405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5162</th>\n",
       "      <td>1.126988</td>\n",
       "      <td>-2.108291</td>\n",
       "      <td>1.045218</td>\n",
       "      <td>-0.445327</td>\n",
       "      <td>-0.687977</td>\n",
       "      <td>0.252229</td>\n",
       "      <td>0.092076</td>\n",
       "      <td>-0.538557</td>\n",
       "      <td>-0.908539</td>\n",
       "      <td>-0.298834</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>0.653021</td>\n",
       "      <td>1.153252</td>\n",
       "      <td>-0.037504</td>\n",
       "      <td>1.043125</td>\n",
       "      <td>0.648524</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5403 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Present_Tmax  LDAPS_RHmax  LDAPS_Tmax_lapse  LDAPS_WS  LDAPS_LH  \\\n",
       "4867      1.837319     0.008303          0.893636 -0.640934  1.733691   \n",
       "4126      0.585783     0.725294          0.895328 -0.713731  0.150235   \n",
       "4361     -0.056898    -1.079991         -0.451831 -0.225430 -1.271137   \n",
       "3605     -0.158374     0.052453          0.415613 -0.603475 -0.870337   \n",
       "6637      0.146054    -0.004252          0.764463 -0.471555 -0.421035   \n",
       "...            ...          ...               ...       ...       ...   \n",
       "905       0.247530     0.650272          0.440318  0.121206 -0.631110   \n",
       "5197      0.721084    -1.888432          0.933176 -0.161507  0.493303   \n",
       "3982      0.146054     0.567746         -0.230874  0.725265 -0.341861   \n",
       "235      -0.699579     0.694980          0.507066  0.092346 -0.701005   \n",
       "5162      1.126988    -2.108291          1.045218 -0.445327 -0.687977   \n",
       "\n",
       "      LDAPS_CC1  LDAPS_CC2  LDAPS_CC3  LDAPS_CC4  LDAPS_PPT1  LDAPS_PPT4  \\\n",
       "4867  -1.365341  -1.365146  -1.063160  -0.055770   -0.305750   -0.224453   \n",
       "4126  -0.835744  -0.119614  -0.193429  -0.834085   -0.305750   -0.224453   \n",
       "4361   0.361864   0.349921   0.342862   1.278165   -0.305750   -0.224453   \n",
       "3605   0.482337   0.654817  -0.293343  -0.397379   -0.305342   -0.224453   \n",
       "6637  -0.711618  -0.673577  -0.707840  -0.905956   -0.305750   -0.224453   \n",
       "...         ...        ...        ...        ...         ...         ...   \n",
       "905   -0.459710  -0.042247   0.527967  -0.678392   -0.305750   -0.224453   \n",
       "5197  -0.438405   0.569373  -0.010130   0.339327   -0.305750   -0.224453   \n",
       "3982   0.753035   1.509653   0.771193  -0.642142   -0.260963   -0.224453   \n",
       "235    2.059803   1.112615   0.543855   0.485321    0.713948   -0.224453   \n",
       "5162   0.252229   0.092076  -0.538557  -0.908539   -0.298834   -0.224453   \n",
       "\n",
       "           lat       lon       DEM     Slope  Solar radiation  \n",
       "4867 -1.221919  0.410460 -0.099918 -0.018790         1.054466  \n",
       "4126  1.189286  0.511177 -0.315157 -0.542158        -0.416905  \n",
       "4361  0.118743 -0.042770  1.294304 -0.484508        -0.988697  \n",
       "3605 -0.685654  0.637074 -0.133199 -0.810993         0.725437  \n",
       "6637  0.653021  1.153252 -0.037504  1.043125         0.803594  \n",
       "...        ...       ...       ...       ...              ...  \n",
       "905  -0.685654  0.637074 -0.133199 -0.810993        -0.091289  \n",
       "5197 -0.149390 -1.263971 -0.852681 -0.803915         0.594264  \n",
       "3982 -1.490051 -1.024766 -0.172266  0.223191        -0.064295  \n",
       "235  -0.149390  1.178432 -0.611095 -0.462470         1.057405  \n",
       "5162  0.653021  1.153252 -0.037504  1.043125         0.648524  \n",
       "\n",
       "[5403 rows x 16 columns]"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "LR = LinearRegression().fit(X_train,Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "std = StandardScaler().fit(Xtrain)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.7423201379511002, 0.7479009954711513)"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "LR.score(X_train,Y_train),LR.score(X_test,Y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.4015376632387949"
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cross_val_score(LR,X,Y,cv=5,scoring='neg_mean_absolute_error').mean()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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